diff --git a/AGENTS.md b/AGENTS.md
index ef1d69f..887cd16 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -66,11 +66,9 @@ Short fixed-iteration run:
 ```bash
 uv run lost-cities-deep-cfr train \
   --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
-  --set run.iterations=100 \
-  --set run.max_hours=null \
-  --set run.max_iterations=null \
-  --set checkpoint.save_latest_only=true \
-  --set checkpoint.save_every_iteration=false
+  --set run.max_iterations=100 \
+  --set run.max_minutes=null \
+  --set checkpoint.save_every=0
 ```
 
 Use explicit run directories for experiments. Put Deep CFR runs under
@@ -95,17 +93,16 @@ Useful train controls:
 - `--resume PATH`: resume from a specific checkpoint.
 - `--set PATH=VALUE`: override config fields. It is repeatable and parses
   values as YAML, e.g. `--set traversal.num_workers=4` or
-  `--set run.max_hours=null`.
+  `--set run.max_minutes=null`.
 
 Common `--set` overrides:
 
 - `--set run.device=cuda`: set the trainer device.
 - `--set checkpoint.exact_resume=true`: require checkpoint config compatibility.
-- `--set checkpoint.save_latest=false --set checkpoint.save_every_iteration=false
-  --set checkpoint.save_iteration_interval=0`: disable checkpoint writes.
-- `--set checkpoint.save_latest_only=true --set checkpoint.save_every_iteration=false`:
-  keep only `latest.pt`.
-- `--set checkpoint.save_iteration_interval=N`: archive every N iterations.
+- `--set checkpoint.save_latest=false --set checkpoint.save_every=0`:
+  disable checkpoint writes.
+- `--set checkpoint.save_every=0`: keep only `latest.pt` (no archives).
+- `--set checkpoint.save_every=N`: archive every N iterations.
 
 ## Long Runs
 
@@ -149,16 +146,15 @@ The unbounded config intentionally has:
 
 ```yaml
 run:
-  iterations: null
   max_iterations: null
-  max_hours: null
+  max_minutes: null
 checkpoint:
-  save_iteration_interval: 100
+  save_every: 100
 ```
 
 `latest.pt` is updated continuously; archive checkpoints are written every 100
-iterations. If disk is tight, prefer `--set checkpoint.save_latest_only=true` or
-increase `save_iteration_interval`.
+iterations. If disk is tight, set `--set checkpoint.save_every=0` (keep only
+`latest.pt`) or increase `save_every`.
 
 ## Evaluation And Analysis
 
diff --git a/configs/deep_cfr/deep_cfr_color_shared_512x3.yaml b/configs/deep_cfr/deep_cfr_color_shared_512x3.yaml
index 1754052..5227752 100644
--- a/configs/deep_cfr/deep_cfr_color_shared_512x3.yaml
+++ b/configs/deep_cfr/deep_cfr_color_shared_512x3.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_color_shared_512x3
-  iterations: null
   seed: 42
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -30,10 +29,10 @@ network:
   color_attention_heads: 4
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -89,9 +87,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_color_shared_512x3
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_color_shared_attention_512x3.yaml b/configs/deep_cfr/deep_cfr_color_shared_attention_512x3.yaml
index 74b25ec..0487c68 100644
--- a/configs/deep_cfr/deep_cfr_color_shared_attention_512x3.yaml
+++ b/configs/deep_cfr/deep_cfr_color_shared_attention_512x3.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_color_shared_attention_512x3
-  iterations: null
   seed: 42
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -30,10 +29,10 @@ network:
   color_attention_heads: 8
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -89,9 +87,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_color_shared_attention_512x3
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter.yaml b/configs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter.yaml
index 4e73f84..752c7b7 100644
--- a/configs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_color_shared_attention_1000iter
-  iterations: null
   seed: 42
   max_iterations: 1000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -30,10 +29,10 @@ network:
   color_attention_heads: 8
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -49,7 +48,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -71,9 +70,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 256
@@ -89,9 +87,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_color_shared_attention_exp_1000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter.yaml b/configs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter.yaml
index d8d42a5..3045dad 100644
--- a/configs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_opponent_average_strategy_512x3_1000iter
-  iterations: null
   seed: 79
   max_iterations: 1000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_opponent_average_strategy_512x3_1000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter.yaml b/configs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter.yaml
index 124f60e..39aed53 100644
--- a/configs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_opponent_network_1024x4_1000iter
-  iterations: null
   seed: 79
   max_iterations: 1000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_opponent_network_1024x4_1000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter.yaml b/configs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter.yaml
index 78f6f73..9676101 100644
--- a/configs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_opponent_network_512x3_1000iter
-  iterations: null
   seed: 79
   max_iterations: 1000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_opponent_network_512x3_1000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml b/configs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
index f4e1436..3fde4b8 100644
--- a/configs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
-  iterations: null
   seed: 79
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_pure_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter.yaml b/configs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter.yaml
index 702d8ed..4501282 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter
-  iterations: null
   seed: 79
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_anchor_safe_512x3_2x_updates_10000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml
index 109eb89..10c350a 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml
@@ -2,7 +2,7 @@ run:
   experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability
   seed: 79
   max_iterations: null
-  max_hours: 4
+  max_minutes: 240
   device: cuda
   use_amp: false
 
@@ -39,7 +39,7 @@ traversal:
   cutoff_rollout_max_steps: 300
   progress_every_traversals: 10
   num_workers: 8
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
   outcome_sampling_value_clip: 500
@@ -87,7 +87,5 @@ evaluation:
 
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability
-  save_every_iteration: false
-  save_iteration_interval: 10
-  save_latest_only: false
+  save_every: 10
   progress_interval_seconds: 20.0
diff --git a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
index 1eedccf..bb44490 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
-  iterations: null
   seed: 79
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 512
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_2x_updates_10000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter.yaml b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter.yaml
index c1dd3a9..1d3ff0d 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter
-  iterations: null
   seed: 79
   max_iterations: 10000
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -69,9 +68,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 256
@@ -87,9 +85,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_lcfr_10000iter
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded.yaml b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded.yaml
index e8e3def..fdd5919 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded
-  iterations: null
   seed: 79
   max_iterations: null
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -27,10 +26,10 @@ network:
   activation: relu
 
 traversal:
-  traversals_per_iteration: 2
+  traversals_per_player: 2
   traversals_per_player: 70
   max_depth: null
-  max_nodes: 10000
+  max_nodes_per_traversal: 10000
   max_nodes_per_traversal: 1000
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
@@ -46,7 +45,7 @@ traversal:
   store_strategy_on_opponent_nodes: false
   num_workers: 8
   worker_chunk_size: 4
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   progress_every_traversals: 10
   endpoint_depth_bucket_width: 100
   endpoint_depth_bucket_max: 1000
@@ -68,9 +67,8 @@ self_play:
   recent_window: 5
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 32
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   advantage_batch_size: 1024
   strategy_batch_size: 1024
   advantage_updates_per_iteration: 256
@@ -86,9 +84,7 @@ memory:
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_512x3_unbounded
   save_latest: true
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
   exact_resume: false
 
diff --git a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml
index 941bec1..f17446c 100644
--- a/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml
+++ b/configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded.yaml
@@ -1,9 +1,8 @@
 run:
   experiment_name: lost_cities_deep_cfr_selfplay_full_depth_slot_playability_unbounded
-  iterations: null
   seed: 79
   max_iterations: null
-  max_hours: null
+  max_minutes: null
   device: cuda
   use_amp: false
 
@@ -40,7 +39,7 @@ traversal:
   cutoff_rollout_max_steps: 300
   progress_every_traversals: 10
   num_workers: 8
-  traversal_worker_chunk_size: 8
+  worker_chunk_size: 8
   regret_matching_epsilon: 0.0001
   outcome_sampling_epsilon: 0.2
   outcome_sampling_value_clip: 500
@@ -88,7 +87,5 @@ evaluation:
 
 checkpoint:
   directory: runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability_unbounded
-  save_every_iteration: false
-  save_iteration_interval: 100
-  save_latest_only: false
+  save_every: 100
   progress_interval_seconds: 20.0
diff --git a/configs/deep_cfr/smoke.yaml b/configs/deep_cfr/smoke.yaml
index ea1d77c..d3cfe74 100644
--- a/configs/deep_cfr/smoke.yaml
+++ b/configs/deep_cfr/smoke.yaml
@@ -1,5 +1,5 @@
 run:
-  iterations: 1
+  max_iterations: 1
   seed: 1
   device: cpu
 
@@ -7,16 +7,17 @@ network:
   hidden_size: 16
 
 traversal:
-  traversals_per_iteration: 1
+  traversals_per_player: 1
   max_depth: 2
-  max_nodes: 64
+  max_nodes_per_traversal: 64
   num_workers: 0
   worker_chunk_size: 1
 
 optimization:
-  advantage_train_steps: 1
-  strategy_train_steps: 1
-  batch_size: 2
+  advantage_batch_size: 2
+  strategy_batch_size: 2
+  advantage_updates_per_iteration: 1
+  strategy_updates_per_iteration: 1
   learning_rate: 0.001
 
 memory:
@@ -25,7 +26,8 @@ memory:
 
 checkpoint:
   directory: runs/deep_cfr/smoke
-  save_every_iteration: false
+  save_every: 0
+  save_latest: false
 
 evaluation:
   eval_every: 0
diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py b/src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py
index f26101f..529167a 100644
--- a/src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py
+++ b/src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py
@@ -13,14 +13,15 @@ def benchmark_traversal(
 ) -> dict[str, float | int]:
     base = config or DeepCFRConfig.model_validate(
         {
-            "run": {"iterations": 1},
-            "traversal": {"traversals_per_iteration": 8, "max_depth": 4},
-            "checkpoint": {"save_every_iteration": False},
+            "run": {"max_iterations": 1},
+            "traversal": {"traversals_per_player": 8, "max_depth": 4},
+            "checkpoint": {"save_every": 0, "save_latest": False},
         }
     )
     data = base.model_dump(mode="python")
     data["traversal"]["num_workers"] = num_workers
-    data["checkpoint"]["save_every_iteration"] = False
+    data["checkpoint"]["save_every"] = 0
+    data["checkpoint"]["save_latest"] = False
     data["evaluation"]["eval_every"] = 0
     cfg = DeepCFRConfig.model_validate(data)
     trainer = DeepCFRTrainer(cfg, cfg.rules.to_lost_cities_config(seed=cfg.run.seed))
diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py b/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py
index 9c6aca5..485eff6 100644
--- a/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py
+++ b/src/coolrl_lost_cities/games/classic/deep_cfr/cli.py
@@ -136,10 +136,10 @@ def benchmark_command(args: argparse.Namespace) -> None:
         {
             "run": {"seed": args.seed},
             "traversal": {
-                "traversals_per_iteration": args.traversals,
+                "traversals_per_player": args.traversals,
                 "max_depth": args.depth,
             },
-            "checkpoint": {"save_every_iteration": False},
+            "checkpoint": {"save_every": 0},
         }
     )
     if args.compare:
@@ -212,7 +212,7 @@ def main(argv: list[str] | None = None) -> None:
         metavar="PATH=VALUE",
         help=(
             "Override config fields using dotted paths. Repeatable. VALUE is parsed as "
-            "YAML, e.g. --set traversal.num_workers=4 --set run.max_hours=null."
+            "YAML, e.g. --set traversal.num_workers=4 --set run.max_minutes=null."
         ),
     )
     train.add_argument(
diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/config.py b/src/coolrl_lost_cities/games/classic/deep_cfr/config.py
index 263fc8b..7e33037 100644
--- a/src/coolrl_lost_cities/games/classic/deep_cfr/config.py
+++ b/src/coolrl_lost_cities/games/classic/deep_cfr/config.py
@@ -18,23 +18,18 @@ class StrictModel(BaseModel):
 
 class RunConfig(StrictModel):
     experiment_name: str = "deep_cfr"
-    iterations: int | None = 1
     max_iterations: int | None = None
-    max_hours: float | None = None
+    max_minutes: float | None = None
     seed: int = 1
-    device: str = "cpu"
+    device: str = "auto"
     use_amp: bool = False
 
     @field_validator("device")
     @classmethod
     def _normalize_device(cls, value: str) -> str:
         token = value.strip().lower()
-        if token == "cuda":
-            return "cuda"
-        if token == "cpu":
-            return "cpu"
-        if token == "auto":
-            return "auto"
+        if token in {"cuda", "cpu", "auto"}:
+            return token
         return token
 
 
@@ -95,12 +90,10 @@ class NetworkConfig(StrictModel):
 
 
 class TraversalConfig(StrictModel):
-    traversals_per_iteration: int = 2
-    traversals_per_player: int | None = None
+    traversals_per_player: int = 8
     sampling_mode: str = "outcome"
-    max_depth: int | None = 8
-    max_nodes: int | None = 10_000
-    max_nodes_per_traversal: int | None = None
+    max_depth: int | None = None
+    max_nodes_per_traversal: int | None = 10_000
     regret_matching_epsilon: float = 1.0e-8
     outcome_sampling_epsilon: float = 0.0
     outcome_sampling_value_clip: float | None = None
@@ -109,13 +102,12 @@ class TraversalConfig(StrictModel):
     cutoff_rollouts: int = 0
     cutoff_rollout_policy: str = "random"
     cutoff_rollout_max_steps: int = 10_000
-    opponent_policy: str = "network"
+    opponent_policy: str = "self_play_league"
     strategy_sample_interval: int = 1
     store_strategy_on_traverser_nodes: bool = True
     store_strategy_on_opponent_nodes: bool = True
     num_workers: int | str = 0
     worker_chunk_size: int = 4
-    traversal_worker_chunk_size: int | None = None
     progress_every_traversals: int = 0
     endpoint_depth_bucket_width: int = 100
     endpoint_depth_bucket_max: int = 1000
@@ -169,21 +161,6 @@ class TraversalConfig(StrictModel):
             workers = max(0, int(self.num_workers))
         return min(workers, batches) if batches is not None and batches > 0 else workers
 
-    def resolved_traversals_per_player(self) -> int:
-        if self.traversals_per_player is not None:
-            return max(0, int(self.traversals_per_player))
-        return max(0, int(self.traversals_per_iteration))
-
-    def resolved_max_nodes(self) -> int | None:
-        if self.max_nodes_per_traversal is not None:
-            return self.max_nodes_per_traversal
-        return self.max_nodes
-
-    def resolved_worker_chunk_size(self) -> int:
-        if self.traversal_worker_chunk_size is not None:
-            return max(1, int(self.traversal_worker_chunk_size))
-        return max(1, int(self.worker_chunk_size))
-
 
 class RegretMatchingConfig(StrictModel):
     all_negative_fallback: str = "uniform"
@@ -225,37 +202,14 @@ class SelfPlayLeagueConfig(StrictModel):
 
 
 class OptimizationConfig(StrictModel):
-    advantage_train_steps: int = 1
-    strategy_train_steps: int = 1
-    batch_size: int = 32
-    advantage_batch_size: int | None = None
-    strategy_batch_size: int | None = None
-    advantage_updates_per_iteration: int | None = None
-    strategy_updates_per_iteration: int | None = None
+    advantage_batch_size: int = 256
+    strategy_batch_size: int = 256
+    advantage_updates_per_iteration: int = 64
+    strategy_updates_per_iteration: int = 64
     learning_rate: float = 1.0e-3
     weight_decay: float = 0.0
     grad_clip: float = 0.0
 
-    def resolved_advantage_batch_size(self) -> int:
-        if self.advantage_batch_size is not None:
-            return max(1, int(self.advantage_batch_size))
-        return max(1, int(self.batch_size))
-
-    def resolved_strategy_batch_size(self) -> int:
-        if self.strategy_batch_size is not None:
-            return max(1, int(self.strategy_batch_size))
-        return max(1, int(self.batch_size))
-
-    def resolved_advantage_train_steps(self) -> int:
-        if self.advantage_updates_per_iteration is not None:
-            return max(0, int(self.advantage_updates_per_iteration))
-        return max(0, int(self.advantage_train_steps))
-
-    def resolved_strategy_train_steps(self) -> int:
-        if self.strategy_updates_per_iteration is not None:
-            return max(0, int(self.strategy_updates_per_iteration))
-        return max(0, int(self.strategy_train_steps))
-
 
 class MemoryConfig(StrictModel):
     advantage_capacity: int = 2_000_000
@@ -264,10 +218,8 @@ class MemoryConfig(StrictModel):
 
 class CheckpointConfig(StrictModel):
     directory: str = "runs/deep_cfr/default"
+    save_every: int = 1
     save_latest: bool = True
-    save_every_iteration: bool = True
-    save_iteration_interval: int = 0
-    save_latest_only: bool = False
     progress_interval_seconds: float = 20.0
     exact_resume: bool = False
 
@@ -277,9 +229,9 @@ class CheckpointConfig(StrictModel):
 
 
 class EvaluationConfig(StrictModel):
-    eval_every: int = 0
+    eval_every: int = 50
     games: int = 10
-    opponents: tuple[str, ...] = ("random",)
+    opponents: tuple[str, ...] = ("random", "safe_heuristic")
     max_steps: int = 10_000
     on_max_steps: str = "score_diff"
     batch_size: int = 64
@@ -302,9 +254,6 @@ class EvaluationConfig(StrictModel):
             raise ValueError("must be 'trainer', 'auto', 'cpu', or 'cuda'")
         return token
 
-    def resolved_batch_size(self) -> int:
-        return max(1, int(self.batch_size))
-
     def resolved_num_workers(self, opponent_count: int | None = None) -> int:
         workers = max(1, int(self.num_workers))
         if opponent_count is not None:
diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py b/src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py
index 9ea9db8..6af1d8b 100644
--- a/src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py
+++ b/src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py
@@ -329,7 +329,7 @@ class DeepCFRTrainer:
         progress_every = int(self.config.traversal.progress_every_traversals)
         completed = 0
         progress_started = time.perf_counter()
-        traversals_per_player = self.config.traversal.resolved_traversals_per_player()
+        traversals_per_player = self.config.traversal.traversals_per_player
         for player in range(2):
             seeds = [
                 self.config.run.seed + iteration * 10_000 + traversal_index * 10 + player
@@ -358,7 +358,7 @@ class DeepCFRTrainer:
                     self.config.traversal.store_strategy_on_opponent_nodes
                 ),
                 max_depth=self.config.traversal.max_depth,
-                max_nodes=self.config.traversal.resolved_max_nodes(),
+                max_nodes=self.config.traversal.max_nodes_per_traversal,
                 sampling_mode=self.config.traversal.sampling_mode,
                 outcome_sampling_epsilon=self.config.traversal.outcome_sampling_epsilon,
                 outcome_sampling_value_clip=self.config.traversal.outcome_sampling_value_clip,
@@ -409,7 +409,7 @@ class DeepCFRTrainer:
         self.tracker.log_event(
             f"Traversal multiprocessing enabled iteration={iteration} "
             f"requested_workers={requested_workers} effective_workers={max_workers} "
-            f"batches={len(batches)} chunk_size={self.config.traversal.resolved_worker_chunk_size()}"
+            f"batches={len(batches)} chunk_size={self.config.traversal.worker_chunk_size}"
         )
         if max_workers < requested_workers:
             self.tracker.log_event(
@@ -481,12 +481,12 @@ class DeepCFRTrainer:
                 name: value.detach().cpu()
                 for name, value in self.strategy_network.state_dict().items()
             }
-        chunk_size = self.config.traversal.resolved_worker_chunk_size()
+        chunk_size = self.config.traversal.worker_chunk_size
         batch_index = 0
         for player in range(2):
             seeds = [
                 self.config.run.seed + iteration * 10_000 + index * 10 + player
-                for index in range(self.config.traversal.resolved_traversals_per_player())
+                for index in range(self.config.traversal.traversals_per_player)
             ]
             for start in range(0, len(seeds), chunk_size):
                 chunk = seeds[start : start + chunk_size]
@@ -574,27 +574,21 @@ class DeepCFRTrainer:
     def _stop_iteration(self) -> int:
         if self.config.run.max_iterations is not None:
             return max(self.iteration, int(self.config.run.max_iterations))
-        if self.config.run.max_hours is not None:
-            return 2**31 - 1
-        if self.config.run.iterations is None:
-            return 2**31 - 1
-        return self.iteration + self.config.run.iterations
+        return 2**31 - 1
 
     def _time_limit_reached(self, run_started: float) -> bool:
-        if self.config.run.max_hours is None:
+        if self.config.run.max_minutes is None:
             return False
-        elapsed_hours = (time.perf_counter() - run_started) / 3600.0
-        return elapsed_hours >= self.config.run.max_hours
+        elapsed_minutes = (time.perf_counter() - run_started) / 60.0
+        return elapsed_minutes >= self.config.run.max_minutes
 
     def _should_save_iteration(self, iteration: int) -> bool:
-        if self.config.checkpoint.save_every_iteration:
-            return True
-        interval = int(self.config.checkpoint.save_iteration_interval)
+        interval = int(self.config.checkpoint.save_every)
         return interval > 0 and iteration % interval == 0
 
     def _save_iteration_checkpoints(self, iteration: int, item: IterationMetrics) -> None:
         checkpoint_dir = self.run_dir
-        if self._should_save_iteration(iteration) and not self.config.checkpoint.save_latest_only:
+        if self._should_save_iteration(iteration):
             self.save_checkpoint(checkpoint_dir / f"iteration_{iteration:05d}.pt", item)
         if self.config.checkpoint.save_latest:
             self.save_checkpoint(checkpoint_dir / "latest.pt", item)
@@ -641,7 +635,7 @@ class DeepCFRTrainer:
                 device=eval_device,
                 max_steps=self.config.evaluation.max_steps,
                 encoding=self.config.encoding,
-                batch_size=self.config.evaluation.resolved_batch_size(),
+                batch_size=self.config.evaluation.batch_size,
             )
             for key, value in result.items():
                 results[f"eval_{opponent}_{key}"] = value
@@ -657,7 +651,7 @@ class DeepCFRTrainer:
         self.tracker.log_event(
             f"Evaluation multiprocessing enabled iteration={iteration} "
             f"effective_workers={max_workers} opponents={len(opponents)} "
-            f"batch_size={self.config.evaluation.resolved_batch_size()} device={eval_device}"
+            f"batch_size={self.config.evaluation.batch_size} device={eval_device}"
         )
         state_dict = self._strategy_state_dict_cpu()
         jobs = [
@@ -673,7 +667,7 @@ class DeepCFRTrainer:
                 seed=self.config.run.seed + iteration * 1000,
                 device=str(eval_device),
                 max_steps=self.config.evaluation.max_steps,
-                batch_size=self.config.evaluation.resolved_batch_size(),
+                batch_size=self.config.evaluation.batch_size,
             )
             for opponent in opponents
         ]
@@ -773,10 +767,10 @@ class DeepCFRTrainer:
     ) -> float:
         losses: list[float] = []
         network.train()
-        for _step in range(self.config.optimization.resolved_advantage_train_steps()):
+        for _step in range(self.config.optimization.advantage_updates_per_iteration):
             sample_started = time.perf_counter()
             batch = self.advantage_memories[player].sample(
-                self.config.optimization.resolved_advantage_batch_size(),
+                self.config.optimization.advantage_batch_size,
                 self.rng,
             )
             self._runtime_metrics[f"advantage_player_{player}_sample_seconds"] = (
@@ -828,10 +822,10 @@ class DeepCFRTrainer:
     ) -> float:
         last_loss = 0.0
         network.train()
-        for _step in range(self.config.optimization.resolved_strategy_train_steps()):
+        for _step in range(self.config.optimization.strategy_updates_per_iteration):
             sample_started = time.perf_counter()
             batch = self.strategy_memory.sample(
-                self.config.optimization.resolved_strategy_batch_size(), self.rng
+                self.config.optimization.strategy_batch_size, self.rng
             )
             self._runtime_metrics["strategy_sample_seconds"] = (
                 float(self._runtime_metrics.get("strategy_sample_seconds", 0.0))
diff --git a/src/coolrl_lost_cities/games/classic/deep_cfr/workers.py b/src/coolrl_lost_cities/games/classic/deep_cfr/workers.py
index 4f94407..634b1b0 100644
--- a/src/coolrl_lost_cities/games/classic/deep_cfr/workers.py
+++ b/src/coolrl_lost_cities/games/classic/deep_cfr/workers.py
@@ -100,7 +100,7 @@ def run_traversal_worker_batch(batch: TraversalWorkerBatch) -> TraversalWorkerRe
         store_strategy_on_traverser_nodes=cfg.traversal.store_strategy_on_traverser_nodes,
         store_strategy_on_opponent_nodes=cfg.traversal.store_strategy_on_opponent_nodes,
         max_depth=cfg.traversal.max_depth,
-        max_nodes=cfg.traversal.resolved_max_nodes(),
+        max_nodes=cfg.traversal.max_nodes_per_traversal,
         sampling_mode=cfg.traversal.sampling_mode,
         outcome_sampling_epsilon=cfg.traversal.outcome_sampling_epsilon,
         outcome_sampling_value_clip=cfg.traversal.outcome_sampling_value_clip,
diff --git a/tests/games/classic/test_deep_cfr_trainer.py b/tests/games/classic/test_deep_cfr_trainer.py
index 8b1ad57..0f4aaef 100644
--- a/tests/games/classic/test_deep_cfr_trainer.py
+++ b/tests/games/classic/test_deep_cfr_trainer.py
@@ -33,9 +33,9 @@ def _deep_cfr_config(data: dict) -> DeepCFRConfig:
 def test_deep_cfr_loads_smoke_yaml_config() -> None:
     config = load_config("configs/deep_cfr/smoke.yaml")
 
-    assert config.run.iterations == 1
+    assert config.run.max_iterations == 1
     assert config.network.hidden_size == 16
-    assert config.traversal.traversals_per_iteration == 1
+    assert config.traversal.traversals_per_player == 1
     assert config.checkpoint.directory == "runs/deep_cfr/smoke"
 
 
@@ -45,30 +45,30 @@ def test_deep_cfr_loads_mapped_legacy_reproduction_config() -> None:
     assert config.run.experiment_name.endswith("slot_playability")
     assert config.run.seed == 79
     assert config.run.max_iterations is None
-    assert config.run.max_hours == 4
+    assert config.run.max_minutes == 240
     assert config.encoding.derived_playability is True
     assert config.encoding.slot_aware_playability is True
     assert config.network.hidden_size == 256
     assert config.network.num_layers == 3
-    assert config.traversal.resolved_traversals_per_player() == 70
+    assert config.traversal.traversals_per_player == 70
     assert config.traversal.sampling_mode == "outcome"
     assert config.traversal.max_depth is None
-    assert config.traversal.resolved_max_nodes() == 1000
-    assert config.traversal.resolved_worker_chunk_size() == 8
+    assert config.traversal.max_nodes_per_traversal == 1000
+    assert config.traversal.worker_chunk_size == 8
     assert config.traversal.progress_every_traversals == 10
-    assert config.optimization.resolved_advantage_batch_size() == 1024
-    assert config.optimization.resolved_strategy_batch_size() == 1024
-    assert config.optimization.resolved_advantage_train_steps() == 256
-    assert config.optimization.resolved_strategy_train_steps() == 256
+    assert config.optimization.advantage_batch_size == 1024
+    assert config.optimization.strategy_batch_size == 1024
+    assert config.optimization.advantage_updates_per_iteration == 256
+    assert config.optimization.strategy_updates_per_iteration == 256
     assert config.optimization.weight_decay == 0.0001
     assert config.optimization.grad_clip == 1.0
     assert config.evaluation.on_max_steps == "score_diff"
-    assert config.evaluation.resolved_batch_size() == 64
+    assert config.evaluation.batch_size == 64
     assert config.evaluation.device == "trainer"
     assert config.evaluation.resolved_num_workers() == 4
     assert config.regret_matching.all_negative_fallback == "uniform"
     assert config.training_weighting.mode == "none"
-    assert config.checkpoint.save_iteration_interval == 10
+    assert config.checkpoint.save_every == 10
     assert (
         config.checkpoint.directory == "runs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability"
     )
@@ -80,17 +80,14 @@ def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None
         (),
         {
             "config_overrides": [
-                "run.iterations=1",
-                "run.max_hours=null",
-                "run.max_iterations=null",
-                "traversal.traversals_per_iteration=1",
-                "traversal.traversals_per_player=null",
+                "run.max_iterations=1",
+                "run.max_minutes=null",
+                "traversal.traversals_per_player=1",
                 "traversal.num_workers=0",
                 "regret_matching.all_negative_fallback=argmax_tiebreak",
                 "training_weighting.mode=lcfr",
                 "checkpoint.save_latest=false",
-                "checkpoint.save_every_iteration=false",
-                "checkpoint.save_iteration_interval=0",
+                "checkpoint.save_every=0",
             ],
         },
     )()
@@ -98,15 +95,13 @@ def test_deep_cfr_train_cli_accepts_run_and_traversal_config_overrides() -> None
 
     overridden = _with_overrides(config, _train_overrides_from_args(args))
 
-    assert overridden.run.iterations == 1
-    assert overridden.run.max_hours is None
-    assert overridden.run.max_iterations is None
-    assert overridden.traversal.traversals_per_player is None
-    assert overridden.traversal.resolved_traversals_per_player() == 1
+    assert overridden.run.max_iterations == 1
+    assert overridden.run.max_minutes is None
+    assert overridden.traversal.traversals_per_player == 1
     assert overridden.traversal.resolved_num_workers() == 0
     assert overridden.regret_matching.all_negative_fallback == "argmax_tiebreak"
     assert overridden.training_weighting.mode == "lcfr"
-    assert overridden.checkpoint.save_every_iteration is False
+    assert overridden.checkpoint.save_every == 0
     assert overridden.checkpoint.save_latest is False
 
 
@@ -123,9 +118,7 @@ def test_deep_cfr_train_cli_checkpoint_save_overrides() -> None:
         {
             "config_overrides": [
                 "checkpoint.save_latest=true",
-                "checkpoint.save_latest_only=true",
-                "checkpoint.save_every_iteration=false",
-                "checkpoint.save_iteration_interval=1",
+                "checkpoint.save_every=1",
             ],
         },
     )()
@@ -133,9 +126,7 @@ def test_deep_cfr_train_cli_checkpoint_save_overrides() -> None:
     overridden = _with_overrides(DeepCFRConfig(), _train_overrides_from_args(args))
 
     assert overridden.checkpoint.save_latest is True
-    assert overridden.checkpoint.save_latest_only is True
-    assert overridden.checkpoint.save_every_iteration is False
-    assert overridden.checkpoint.save_iteration_interval == 1
+    assert overridden.checkpoint.save_every == 1
 
 
 def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
@@ -146,8 +137,8 @@ def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
             "config_overrides": [
                 "traversal.sampling_mode=external",
                 "traversal.max_depth=null",
-                "optimization.batch_size=64",
-                "checkpoint.save_latest_only=true",
+                "optimization.advantage_batch_size=64",
+                "checkpoint.save_latest=true",
             ],
         },
     )()
@@ -156,17 +147,17 @@ def test_deep_cfr_train_cli_accepts_generic_config_overrides() -> None:
 
     assert overridden.traversal.sampling_mode == "external"
     assert overridden.traversal.max_depth is None
-    assert overridden.optimization.batch_size == 64
-    assert overridden.checkpoint.save_latest_only is True
+    assert overridden.optimization.advantage_batch_size == 64
+    assert overridden.checkpoint.save_latest is True
 
 
 def test_deep_cfr_iteration_weights_use_sample_age() -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 12},
+                "run": {"max_iterations": 1, "seed": 12},
                 "network": {"hidden_size": 16},
-                "checkpoint": {"save_every_iteration": False},
+                "checkpoint": {"save_every": 0},
                 "training_weighting": {"mode": "lcfr", "lcfr_alpha": 1.0},
             }
         ),
@@ -279,16 +270,21 @@ def test_deep_cfr_slot_aware_playability_encoding_zero_fills_empty_hand_slots()
 def test_deep_cfr_trainer_uses_playability_encoding() -> None:
     config = _deep_cfr_config(
         {
-            "run": {"iterations": 1, "seed": 62},
+            "run": {"max_iterations": 1, "seed": 62},
             "encoding": {"derived_playability": True, "slot_aware_playability": True},
             "network": {"hidden_size": 16},
-            "traversal": {"traversals_per_iteration": 1, "max_depth": 1, "max_nodes": 16},
-            "optimization": {
-                "advantage_train_steps": 1,
-                "strategy_train_steps": 1,
-                "batch_size": 2,
+            "traversal": {
+                "traversals_per_player": 1,
+                "max_depth": 1,
+                "max_nodes_per_traversal": 16,
             },
-            "checkpoint": {"save_every_iteration": False},
+            "optimization": {
+                "advantage_updates_per_iteration": 1,
+                "strategy_updates_per_iteration": 1,
+                "advantage_batch_size": 2,
+                "strategy_batch_size": 2,
+            },
+            "checkpoint": {"save_every": 0},
         }
     )
     game_config = LostCitiesConfig(seed=62)
@@ -317,17 +313,17 @@ def test_deep_cfr_trainer_forwards_metrics_to_extra_trackers(tmp_path) -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 71},
+                "run": {"max_iterations": 1, "seed": 71},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 16,
+                    "max_nodes_per_traversal": 16,
                 },
-                "optimization": {"batch_size": 2},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
                 "checkpoint": {
                     "directory": str(tmp_path / "extra-tracker"),
-                    "save_every_iteration": False,
+                    "save_every": 0,
                 },
             }
         ),
@@ -347,19 +343,20 @@ def test_deep_cfr_trainer_smoke_run() -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 23},
+                "run": {"max_iterations": 1, "seed": 23},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 3,
-                    "max_nodes": 64,
+                    "max_nodes_per_traversal": 64,
                 },
                 "optimization": {
-                    "advantage_train_steps": 1,
-                    "strategy_train_steps": 1,
-                    "batch_size": 2,
+                    "advantage_updates_per_iteration": 1,
+                    "strategy_updates_per_iteration": 1,
+                    "advantage_batch_size": 2,
+                    "strategy_batch_size": 2,
                 },
-                "checkpoint": {"save_every_iteration": False},
+                "checkpoint": {"save_every": 0},
             }
         ),
         LostCitiesConfig(seed=23),
@@ -383,20 +380,21 @@ def test_deep_cfr_trainer_supports_lcfr_and_dcfr_loss_weighting() -> None:
         trainer = DeepCFRTrainer(
             _deep_cfr_config(
                 {
-                    "run": {"iterations": 1, "seed": 24},
+                    "run": {"max_iterations": 1, "seed": 24},
                     "network": {"hidden_size": 16},
                     "traversal": {
-                        "traversals_per_iteration": 1,
+                        "traversals_per_player": 1,
                         "max_depth": 2,
-                        "max_nodes": 32,
+                        "max_nodes_per_traversal": 32,
                     },
                     "optimization": {
-                        "advantage_train_steps": 1,
-                        "strategy_train_steps": 1,
-                        "batch_size": 2,
+                        "advantage_updates_per_iteration": 1,
+                        "strategy_updates_per_iteration": 1,
+                        "advantage_batch_size": 2,
+                        "strategy_batch_size": 2,
                     },
                     "training_weighting": {"mode": mode},
-                    "checkpoint": {"save_every_iteration": False},
+                    "checkpoint": {"save_every": 0},
                 }
             ),
             LostCitiesConfig(seed=24),
@@ -413,15 +411,15 @@ def test_deep_cfr_cython_traverser_restores_state_and_collects_samples() -> None
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 29},
+                "run": {"max_iterations": 1, "seed": 29},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                 },
-                "optimization": {"batch_size": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
+                "checkpoint": {"save_every": 0},
             }
         ),
         LostCitiesConfig(seed=29),
@@ -457,12 +455,12 @@ def test_deep_cfr_cython_traverser_supports_outcome_sampling_and_rollout_cutoffs
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 31},
+                "run": {"max_iterations": 1, "seed": 31},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 1,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                     "outcome_sampling_epsilon": 0.25,
                     "outcome_sampling_value_clip": 100.0,
                     "outcome_unsampled_regret": "zero",
@@ -471,8 +469,8 @@ def test_deep_cfr_cython_traverser_supports_outcome_sampling_and_rollout_cutoffs
                     "cutoff_rollout_policy": "random",
                     "cutoff_rollout_max_steps": 16,
                 },
-                "optimization": {"batch_size": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
+                "checkpoint": {"save_every": 0},
             }
         ),
         LostCitiesConfig(seed=31),
@@ -512,16 +510,16 @@ def test_deep_cfr_cython_traverser_supports_external_sampling() -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 33},
+                "run": {"max_iterations": 1, "seed": 33},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "sampling_mode": "external",
                     "max_depth": 1,
-                    "max_nodes": 64,
+                    "max_nodes_per_traversal": 64,
                 },
-                "optimization": {"batch_size": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
+                "checkpoint": {"save_every": 0},
             }
         ),
         LostCitiesConfig(seed=33),
@@ -563,11 +561,11 @@ def test_deep_cfr_cython_traverser_records_regret_fallback_metrics() -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 37},
+                "run": {"max_iterations": 1, "seed": 37},
                 "network": {"hidden_size": 16},
-                "traversal": {"traversals_per_iteration": 1, "max_depth": 1},
-                "optimization": {"batch_size": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "traversal": {"traversals_per_player": 1, "max_depth": 1},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
+                "checkpoint": {"save_every": 0},
                 "regret_matching": {"all_negative_fallback": "argmax_tiebreak"},
             }
         ),
@@ -637,17 +635,17 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 41},
+                "run": {"max_iterations": 1, "seed": 41},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                 },
-                "optimization": {"batch_size": 2},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
                 "checkpoint": {
                     "directory": str(checkpoint_dir),
-                    "save_every_iteration": True,
+                    "save_every": 1,
                 },
                 "evaluation": {"eval_every": 1, "games": 2, "opponents": ("random",)},
             }
@@ -664,7 +662,7 @@ def test_deep_cfr_trainer_saves_loads_and_evaluates_checkpoint(tmp_path) -> None
                 "network": {"hidden_size": 16},
                 "checkpoint": {
                     "directory": str(checkpoint_dir),
-                    "save_every_iteration": False,
+                    "save_every": 0,
                 },
             }
         ),
@@ -700,13 +698,12 @@ def test_deep_cfr_trainer_always_saves_latest_checkpoint(tmp_path) -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 42},
+                "run": {"max_iterations": 1, "seed": 42},
                 "network": {"hidden_size": 16},
-                "traversal": {"traversals_per_iteration": 1, "max_depth": 1},
+                "traversal": {"traversals_per_player": 1, "max_depth": 1},
                 "checkpoint": {
                     "directory": str(checkpoint_dir),
-                    "save_every_iteration": False,
-                    "save_iteration_interval": 10,
+                    "save_every": 10,
                 },
             }
         ),
@@ -724,10 +721,10 @@ def test_deep_cfr_exact_resume_is_explicitly_not_implemented(tmp_path) -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 44},
+                "run": {"max_iterations": 1, "seed": 44},
                 "network": {"hidden_size": 16},
-                "traversal": {"traversals_per_iteration": 1, "max_depth": 1},
-                "checkpoint": {"directory": str(checkpoint_dir), "save_every_iteration": True},
+                "traversal": {"traversals_per_player": 1, "max_depth": 1},
+                "checkpoint": {"directory": str(checkpoint_dir), "save_every": 1},
             }
         ),
         LostCitiesConfig(seed=44),
@@ -755,20 +752,20 @@ def test_deep_cfr_trainer_multiprocessing_smoke_run(tmp_path) -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 1, "seed": 43},
+                "run": {"max_iterations": 1, "seed": 43},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                     "num_workers": 8,
                     "worker_chunk_size": 1,
                     "progress_every_traversals": 1,
                 },
-                "optimization": {"batch_size": 2},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
                 "checkpoint": {
                     "directory": str(tmp_path / "mp"),
-                    "save_every_iteration": False,
+                    "save_every": 0,
                 },
             }
         ),
@@ -791,8 +788,8 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
             {
                 "run": {"seed": 47},
                 "network": {"hidden_size": 16},
-                "traversal": {"traversals_per_iteration": 1, "max_depth": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "traversal": {"traversals_per_player": 1, "max_depth": 2},
+                "checkpoint": {"save_every": 0},
             }
         )
     )
@@ -804,8 +801,8 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
             {
                 "run": {"seed": 48},
                 "network": {"hidden_size": 16},
-                "traversal": {"traversals_per_iteration": 1, "max_depth": 2},
-                "checkpoint": {"save_every_iteration": False},
+                "traversal": {"traversals_per_player": 1, "max_depth": 2},
+                "checkpoint": {"save_every": 0},
             }
         )
     )
@@ -816,12 +813,12 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 2, "seed": 53},
+                "run": {"max_iterations": 2, "seed": 53},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                     "opponent_policy": "self_play_league",
                 },
                 "self_play": {
@@ -829,10 +826,10 @@ def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None:
                     "max_snapshots": 1,
                     "anchor_probability": 1.0,
                 },
-                "optimization": {"batch_size": 2},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
                 "checkpoint": {
                     "directory": str(tmp_path / "league"),
-                    "save_every_iteration": False,
+                    "save_every": 0,
                 },
             }
         ),
@@ -849,12 +846,12 @@ def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> No
     trainer = DeepCFRTrainer(
         _deep_cfr_config(
             {
-                "run": {"iterations": 2, "seed": 59},
+                "run": {"max_iterations": 2, "seed": 59},
                 "network": {"hidden_size": 16},
                 "traversal": {
-                    "traversals_per_iteration": 1,
+                    "traversals_per_player": 1,
                     "max_depth": 2,
-                    "max_nodes": 32,
+                    "max_nodes_per_traversal": 32,
                     "opponent_policy": "self_play_league",
                 },
                 "self_play": {
@@ -866,10 +863,10 @@ def test_deep_cfr_weighted_self_play_league_uses_snapshot_bucket(tmp_path) -> No
                     "anchor_weight": 0.0,
                     "recent_window": 1,
                 },
-                "optimization": {"batch_size": 2},
+                "optimization": {"advantage_batch_size": 2, "strategy_batch_size": 2},
                 "checkpoint": {
                     "directory": str(tmp_path / "weighted-league"),
-                    "save_every_iteration": False,
+                    "save_every": 0,
                 },
             }
         ),