chess-qwen35-0.8b-rl-20260710 — RL campaign checkpoints

Four checkpoints from the RL phase (2026-07-09/10), all descending from the v3 SFT curriculum (...sft-v3-20260709). Gameplay scores are vs Stockfish limited to Elo 1320, 1,024 games, thinking mode unless noted.

subfolder	what it is	game score
`topup`	v3-C + prompt-unified seed-pack top-up (the RL seed)	0.1160 think / 0.1201 gut
`dpo_r2`	+ iterative DPO on 30.8k stratified self-play pairs	0.1337 think
`r5e_english`	dpo_r2 + English-voice SFT top-up (verified 35B traces) — reasons in natural English (399/400 game traces), best diversity	0.1260 think (statistical tie w/ dpo_r2)
`dopd_r1`	r5e + 200 steps DOPD token-routed distillation (arXiv 2606.30626 adaptation, same-weights privileged teacher)	pilot — see repo docs

Key findings encoded in these checkpoints: thinking-mode play only pays when trained-for (+0.018 for dpo_r2, nothing for the seed); preference training nudges move choice but cannot shift trace style; one SFT top-up flipped the model to natural-English reasoning at zero strength cost. Full records: docs/experiments/2026-07-10_rl_campaign/ in the project repo.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained(
    "Chess-Nut-Engine/chess-qwen35-0.8b-rl-20260710",
    subfolder="r5e_english", trust_remote_code=True)
t = AutoTokenizer.from_pretrained(
    "Chess-Nut-Engine/chess-qwen35-0.8b-rl-20260710",
    subfolder="r5e_english")

Planning protocol: <think>...</think><move>uci</move>; <move> tags are tokenizer special tokens. Thinking mode requires the chat template's enable_thinking=True.

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