Experiment-only prototype for docs/plans/archive/option_b_interleaved_traversal.md. It does not wire into the trainer and does not replace the production Cython recursive traversal path.

The prototype implements a small outcome-sampling traversal subset twice:

recursive baseline: one policy request per forward (bs=1)
explicit continuation scheduler: many traversal contexts yield at policy states, then policy requests are batched and scattered back to contexts

To make interleaving parity checkable, each traversal context owns its own RNG state. This is intentionally stricter for the prototype scheduler and not a claim that production Cython parity is solved.

Run:

uv run python experiments/option_b_interleaved_traversal/prototype_interleaved.py \
  --traversals 64 \
  --interleave-width 32 \
  --max-depth 8 \
  --max-nodes 512 \
  --device cpu

CUDA spot check:

uv run python experiments/option_b_interleaved_traversal/prototype_interleaved.py \
  --traversals 64 \
  --interleave-width 32 \
  --max-depth 8 \
  --max-nodes 512 \
  --device cuda \
  --output experiments/option_b_interleaved_traversal/results_cuda.json

2026-05-07 results, legacy/deep-cfr/configs/default.yaml, RTX 3090 host:

Device	Mode	total s	forward s	scheduler s	batch mean	batch max	speedup
CPU	recursive	0.084	0.054	-	1.0	1	1.00x
CPU	interleaved	0.018	0.005	0.003	32.0	32	4.71x
CUDA	recursive	0.195	0.144	-	1.0	1	1.00x
CUDA	interleaved	0.028	0.014	0.003	32.0	32	6.98x

Interpretation: the scheduling shape works in the prototype. Explicit continuations can raise realized policy batch size from 1 to the interleave width while preserving prototype value/stat/sample parity. The next risk is not whether batching can be formed; it is whether the full production Cython CFR state machine can be represented safely with the same continuation discipline.