Document Deep CFR reproducibility policy

docs/research/deep-cfr-reproducibility-policy.md

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+# Deep CFR Reproducibility Policy
+
+**Last verified:** 2026-05-08, commit `0f85fa8`
+**Source:** `docs/research/deep-cfr-reproducibility.md`
+
+## Policy
+
+Deep CFR experiments should target two different kinds of reproducibility:
+
+1. **Debug reproducibility:** short runs should be bitwise reproducible on the
+   same machine when using deterministic settings.
+2. **Research reproducibility:** reported conclusions should survive repeated
+   seeds and reasonable hardware differences, even when exact metric rows do
+   not match bit-for-bit.
+
+Do not treat a single seeded multi-worker GPU run as a final research result.
+Use it as an exploratory signal unless it is confirmed with matched seeds.
+
+## Hardware Expectations
+
+Exact same metrics are not guaranteed across machines, even with the same CUDA
+version. GPU model, driver, PyTorch build, CPU scheduling, multiprocessing
+timing, and floating-point reduction order can all affect exact trajectories.
+
+Expected reliability by setting:
+
+| Setting | Expected result |
+| --- | --- |
+| Same machine, same GPU, same code, `traversal.num_workers=1` | Best current option for bitwise debug checks |
+| Same machine, same GPU, same code, multi-worker traversal | Current implementation target: bitwise stable across repeated runs |
+| Same GPU/CUDA but different CPU | Current implementation target: same seed should follow the same logical trajectory |
+| Different GPU or PyTorch/CUDA build | Exact equality is not expected; use multi-seed conclusions |
+
+## Required Experiment Practice
+
+For exploratory experiments:
+
+- Single-seed runs are acceptable.
+- Record the resolved config, run directory, W&B URL when used, and final eval
+  metrics.
+- Label conclusions as provisional.
+
+For claims worth keeping:
+
+- Use matched seeds across baseline and treatment.
+- Use at least 3 seeds; prefer 5 when runtime allows.
+- Report mean and standard deviation for the key metrics.
+- Keep `run.seed`, commit SHA, GPU, CUDA/PyTorch versions, and
+  `traversal.num_workers` visible in the run record.
+
+Example matched-seed comparison:
+
+```text
+baseline:  seeds 79, 80, 81
+treatment: seeds 79, 80, 81
+```
+
+Compare the treatment against the baseline seed-by-seed, then report aggregate
+statistics.
+
+## Batch Size Interpretation
+
+Not every batch size is a reproducibility-neutral setting.
+
+Batching that should be algorithm-neutral under the deterministic target:
+
+- traversal/interleaved policy inference batch size, such as
+  `traversal.interleave_max_batch`
+- evaluation inference batch size, such as `evaluation.batch_size`
+
+These settings should affect throughput, not the logical trajectory. Same code,
+seed, hardware stack, and training config should produce the same non-timing
+metrics when only these batching knobs change.
+
+Batching that is an algorithmic training setting:
+
+- `optimization.advantage_batch_size`
+- `optimization.strategy_batch_size`
+- update counts or optimizer schedule parameters
+
+These settings change gradient estimates or optimizer updates. They are
+experiment variables, not reproducibility-neutral execution settings. A run with
+training mini-batch size 3 is not expected to match one with training mini-batch
+size 7.
+
+## Debug Mode
+
+Use debug mode when checking exact reproducibility or suspected regressions:
+
+- `traversal.num_workers=1`
+- `evaluation.num_workers=1`
+- PyTorch deterministic settings enabled:
+  - `torch.use_deterministic_algorithms(True)`
+  - `torch.backends.cudnn.benchmark = False`
+  - `torch.backends.cuda.matmul.allow_tf32 = False`
+  - `torch.backends.cudnn.allow_tf32 = False`
+- short `run.max_iterations`, usually 3
+- checkpoint writes disabled unless specifically needed
+- W&B disabled
+
+The expected check is to run the same command twice and compare non-timing
+metrics in `metrics.jsonl`, including:
+
+- `traversal/nodes`
+- `memory/advantage`
+- `memory/strategy`
+- `loss/advantage`
+- `loss/strategy`
+- eval scores, if evaluation is enabled
+
+Timing fields are not expected to match exactly.
+
+## Implementation Priorities
+
+Current implementation target:
+
+1. Add a small reproducibility smoke script that runs two short debug jobs and
+   compares non-timing metrics.
+2. Make worker count a performance setting, not an algorithm setting:
+   `traversal.num_workers=1`, `4`, and `8` should produce the same non-timing
+   metrics on the same code/config/seed.
+3. Assign stable traversal IDs, derive RNG streams from traversal IDs, and merge
+   samples by traversal order rather than worker, batch, or completion order.
+4. Stabilize interleaved scheduler request/context ordering so CPU scheduling
+   differences do not change the logical traversal path.
+5. Enable PyTorch deterministic settings for reproducibility/debug runs. Keep
+   the speed impact explicit when comparing runtime.
+6. Re-test same-seed runs across `traversal.num_workers=1`, `4`, and `8`.
+7. Re-test on the same GPU/CUDA stack with a different CPU when available.
+
+## Current Interpretation Rule
+
+Until traversal-ID based deterministic scheduling and merge are implemented,
+default multi-worker runs with the same seed may diverge. Interpret exact curves
+cautiously. For research conclusions, prefer matched multi-seed comparisons over
+bitwise row matching.