Honour all_negative_fallback in interleaved scheduler; sync default.yaml

The interleaved traversal scheduler's _regret_matching was hard-coded to
spread fallback policy uniformly across legal actions, regardless of the
configured regret_matching.all_negative_fallback. default.yaml has
shipped with all_negative_fallback: argmax_tiebreak since 618d5f8 based
on the 20-iter audit + 1000-iter empirical comparison in
docs/archive/deep-cfr-regret-fallback-audit-2026-05-07.md, but the
default scheduler was switched to interleaved in 09bbe7c, after which
the configured fallback mode silently no-op'd.

_regret_matching now takes fallback_mode and concentrates policy mass on
the lowest-index tied action when "argmax_tiebreak". Tiebreak is
deterministic; the Cython recursive traverser randomises ties using its
per-traverser RNG, which the batched policy does not have. Behaviour
matches the spirit of the recursive path (concentrate on best, do not
dilute uniformly).

Plumbed through BatchedPolicy, InterleavedTraversalConfig,
run_interleaved_traversal_batch, trainer.py, workers.py, and the
analyze_first_open_targets.py caller. Two unit tests added.

Also bumps default.yaml outcome_sampling_epsilon 0.2 -> 0.05. The
200-iter sweep in docs/plans/deep-cfr-selectivity.md section 1 showed
0.05 produced the best short-run safe_heuristic_strict score diff
(-40.01 vs -57.87 for 0.20). Recent experiments already used 0.05; the
default now matches actual experimental practice.

Neither change targets the diagnosed selection-bias bottleneck. They
align config intent with scheduler behaviour and make the default config
reproduce known-best knob settings out of the box.

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

scripts/analyze_first_open_targets.py

@@ -348,6 +348,7 @@ def analyze_checkpoint(
         outcome_unsampled_first_open_prior_alpha=getattr(
             cfg.traversal, "outcome_unsampled_first_open_prior_alpha", 0.0
         ),
+        all_negative_fallback=cfg.regret_matching.all_negative_fallback,
         max_depth=cfg.traversal.max_depth,
         max_nodes=cfg.traversal.max_nodes_per_traversal,
         strategy_sample_interval=cfg.traversal.strategy_sample_interval,