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docs/archive/deep-cfr-evaluation-profile-plan.md

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+# Deep CFR Evaluation Profile Plan
+
+Goal: split evaluation runtime into the main per-step costs so eval iteration
+spikes can be explained without guessing.
+
+The profiling run should keep the normal full config and `eval_every: 5`, then
+compare iterations 5 and 10.
+
+## Metrics
+
+Each metric is emitted with the existing opponent prefix:
+`eval_<opponent>_<metric_name>`.
+
+Top-level counters:
+
+- `policy_turns`
+- `opponent_turns`
+- `avg_game_length`
+- `elapsed_seconds`
+- `games_per_second`
+- `steps_per_second`
+
+Step-level runtime:
+
+- `policy_select_seconds`
+- `opponent_act_seconds`
+- `apply_action_seconds`
+- `diagnostics_seconds`
+- `final_scoring_seconds`
+
+Policy select breakdown:
+
+- `policy_legal_mask_seconds`
+- `policy_encoding_seconds`
+- `policy_network_seconds`
+- `policy_postprocess_seconds`
+
+## Interpretation
+
+If `policy_network_seconds` dominates, evaluation is mostly batch-size-1 model
+inference overhead.
+
+If `policy_encoding_seconds` or `policy_legal_mask_seconds` dominates, the eval
+policy path is paying per-turn state feature/mask construction costs.
+
+If `opponent_act_seconds` dominates, the opponent bot implementation is the
+main eval cost for that opponent.
+
+If `apply_action_seconds` dominates, the game step path itself is the eval
+bottleneck.