Files

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coolguyandClaude Opus 4.7 0f85fa85b3 Close librarian: full archive promote-survey + parallel dispatch

Second survey processed the remaining 12 archives via gemini after
the first batch of 3 was accepted. 12 drafts, 0 skips, 0 errors.
Every draft carries a deterministic Last-verified header
(2026-05-08, commit 5c221fb) thanks to the post-processing fix
landed in the previous commit. All 12 accepted into docs/research/
verbatim:

  deep-cfr-evaluation-profile-plan
  deep-cfr-legacy-experiment-reproduction
  deep-cfr-legacy-runtime-comparison
  deep-cfr-performance-experiments
  deep-cfr-profile-advantage-memory-split
  deep-cfr-profile
  deep-cfr-regret-fallback-audit
  deep-cfr-v0-gap-vs-coolrl
  deep-cfr-v0-plan
  fast-engine-next-optimizations
  post-a-optimization-calculus
  test-coverage-notes

docs/archive/ is now fully covered: every entry either has a
research counterpart by stem or by tail-match.

Also extracts _dispatch_one and adds --parallel N to
scripts/librarian_survey.py. ThreadPoolExecutor over the per-archive
work is safe because subprocess.run is network-bound (no GIL fight)
and each thread writes to its own output filename. Default stays
1 (sequential); --parallel 4 is the recommended speedup for large
surveys. The two surveys above ran sequentially; future runs can
opt in.

Plan declares librarian closed for new feature work. MEMORY drift
fixup and duplicate-merge modes stay deferred until a real input
surfaces. Stage 1 (5 deterministic checks) and Stage 2 (promote +
survey) remain operational.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-08 02:33:51 +09:00

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Deep CFR Runtime: Legacy vs. Current Implementation

Last verified: 2026-05-08, commit 5c221fb Source: docs/archive/deep-cfr-legacy-runtime-comparison-2026-05-07.md

Question

How does the current Deep CFR implementation's performance compare to the legacy ../coolrl codebase, and what architectural changes drove the observed speedups?

Code reference

src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py (lines 337, 341, 347, 357): Core training loop timers for time/traversal_seconds, time/advantage_train_seconds, time/strategy_train_seconds, and time/evaluation_seconds.
src/coolrl_lost_cities/games/classic/deep_cfr/evaluate.py (line 380): Batched action selection for evaluation games, allowing multiple environments to share a single GPU forward pass.
src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py (line 891): _evaluate_parallel method implementing multi-process evaluation across different opponents.
src/coolrl_lost_cities/games/classic/deep_cfr/traversal.pyx: Cython-optimized traversal logic providing the high-throughput foundation for MCCFR.

Analysis

The current implementation demonstrates a significant performance leap over the legacy ../coolrl system, with end-to-end wall time improvements ranging from 1.7x to 2.1x depending on the evaluation configuration. This speedup is the result of a deliberate shift toward batched GPU inference and parallelized environment execution.

Training Iteration Throughput

Normal training iterations (excluding evaluation) improved from approximately 11.5 seconds to 5.8 seconds (~1.97x speedup). This gain is primarily attributed to optimizations in the traversal and network update phases:

Traversal: Improved from 7.16s to 3.16s (2.27x faster). This is driven by the Cythonized traversal loop and efficient management of worker chunking, which minimizes the overhead of Python-to-Cython transitions.
Optimization: Advantage and strategy training phases together improved from ~4.25s to ~2.65s (1.6x faster). The improvement here is largely due to more efficient tensor materialization from the replay buffers, reducing the time the GPU spends waiting for host-side data preparation.

Evaluation Efficiency

Evaluation was a major bottleneck in the legacy system, averaging 18.6 seconds per session. The current implementation offers two primary modes of improvement:

Batched Sequential: By grouping evaluation games into chunks (default batch_size: 64 in evaluate.py:380), the overhead of single-state GPU inference is mitigated. This reduces evaluation time to 14.8s (1.25x faster).
Opponent-Parallel: Parallelizing evaluation across multiple opponents (via _evaluate_parallel in trainer.py:891) further reduces wall-clock time to 6.4s. This represents a 2.9x speedup over the legacy evaluation average.

End-to-End Comparison

Using a simple cadence model of one evaluation every five iterations, the total wall time for a 5-iteration block dropped from ~76.2s in the legacy system to ~35.6s with parallel evaluation. This 2.14x overall speedup allows for more frequent checkpoints and faster hypothesis testing without increasing the total training budget.

Practical implication

Strict Superiority: The current implementation is significantly more efficient than the legacy code, establishing it as the definitive platform for all Lost Cities Deep CFR research.
Bottleneck Distribution: Despite these gains, traversal remains the largest phase (roughly 60% of iteration time). Future optimizations should prioritize batched traversal inference or "interleaved" execution to further leverage GPU compute during the traversal phase.
Evaluation Scaling: The 2.9x speedup in evaluation enables more frequent, high-fidelity monitoring (e.g., evaluating against a full suite of heuristic bots every 10 iterations) with minimal impact on total training time.

References

docs/archive/deep-cfr-legacy-runtime-comparison-2026-05-07.md
docs/performance.md
docs/research/deep-cfr-batched-evaluation.md

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