model card

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-0.8B
+tags:
+  - chess
+  - rl
+  - dpo
+  - distillation
+  - qwen3.5
+datasets:
+  - Chess-Nut-Engine/chess-sft-corpus-4x
+---
+
+# 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
+
+```python
+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`.