Add a microbench that separates game/encoding overhead, single-request policy boundary overhead, and batched PyTorch forward lower bounds. Record CPU/CUDA results and link the finding from the Julia port evaluation.
Co-Authored-By: Codex <codex@openai.com>
Captures the dependency graph between pending levers (model size,
Option B, AMP, compile, TensorRT, Option A re-enable, Julia port) and
the rule that infrastructure optimization precedes the model-size
experiment because every future training run benefits from the
infrastructure speedup, not just the one keystone experiment.
ideas.md gets a third Active Research Threads pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Decision-in-advance for criteria 3 (multi-thread scaling), 4 (Flux/CUDA
MLP forward), and 5 (real-game-state slice). Each criterion lists PASS
/ PARTIAL / FAIL bands with concrete numerical thresholds, plus a
decision rule that maps {3, 4, 5} outcomes to a single action: port,
hybrid (Julia traversal + PyTorch networks), or stay on Python/Cython
and pursue Option B instead.
Cost-of-being-wrong asymmetry stated explicitly: port is months,
staying is zero work, so the GO bar is deliberately above 50% and the
STAY bar is permissive.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
experiments/julia_cfr_toy/ ports a synthetic CFR external-sampling
traversal to both Julia and Cython for direct head-to-head
measurement of the actual project hot-path pattern (recursive
tree + mutable regret state + branch-heavy legal-action logic).
Headline result (2026-05-07, single run): Julia ~1.9× faster than
Cython on this pattern, 0 MB allocation, 0% GC time. Root regret
parity ε ≤ 1e-9. The GC-pause concern that was the main argument
against Julia adoption did not materialize. Cython's 21.3 MB
allocation suggests its implementation can be tightened, so the
honest gap window is roughly 1.3×–1.9×.
Multi-thread scaling (bench_cfr_threaded.jl): 2.44× wall-clock at 8T
but only 31% efficiency — inconclusive, likely a toy-size artifact
(per-thread workload too small to amortize dispatch). A heavier
per-thread workload sweep is the remaining decisive test.
docs/research/julia_port_evaluation.md captures this evidence
alongside the earlier safe-heuristic single-thread parity result
and lists the remaining decision criteria (multi-thread scaling with
heavier workload, Flux.jl+CUDA.jl coverage, real-game-state slice).
Do not commit to porting until multi-thread scaling is conclusively
settled.
ideas.md gets a second Active Research Threads pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mechanism-level analysis of the slot_aware_playability iter 240
plateau (opened_colors 4.95+, bad_open_rate 88-92%, calibration
gap 6-9 → 2-4). Captures four expert consultations with diagnostic
hypotheses, intervention catalog (architectural / training-dynamics
/ game-specific), measurement plan, and a comparison table across
the four sources.
Key new directions surfaced:
- Current vs average vs league policy separation (Deep CFR average
strategy is the convergence target, not advantage current).
- All-negative fallback as Deep CFR ablation lever.
- Empirical r̃ partitioning by action class.
- Tabular Lost Cities oracle as a clean test of "is 5-color the
game-theoretic answer or an approximation artifact".
- Entry-gate target defined from traversal counterfactual values
instead of heuristic labels.
ideas.md gets an "Active Research Threads" pointer.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
OpenSpiel's Deep CFR records strategy samples at opponent nodes during
the traverser's tree walk; storing on traverser nodes under external
sampling drops the ρ_p reach factor and biases the average-policy
estimate. Reject that config combination at load time and add a research
note deriving why outcome sampling is unaffected while external is not.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Five notes covering outcome-sampling target correctness, package
architecture, v0 feature-parity vs legacy, opponent-policy network
divergence, and regret-matching fallback audit. Four are derived from
archive sources (cited via Source: lines); outcome-sampling-target is
a fresh write-up and serves as the style template.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguyandCodex