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>
coolguyandClaude Opus 4.7