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UofTCSSLab
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Model Details
Model Provider
UofTCSSLab
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
900-puzzle test set, greedy pass@1, FINAL_ANSWER exact-match UCI:
| stage | accuracy |
|---|---|
| SFT (base for RL) | 42.3% |
| RL (this model) | 48.3% |
Average response length ~169 tokens.
The prompt gives the FEN, piece positions, and legal moves, then asks for a
step-by-step analysis ending in FINAL_ANSWER: <uci_move>. Greedy decoding
(temperature 0) is recommended.
python
# pip install chessimport chess def build_prompt(fen: str) -> str: board = chess.Board(fen) names = {1: "Pawn", 2: "Knight", 3: "Bishop", 4: "Rook", 5: "Queen", 6: "King"} pieces = {} for sq in chess.SQUARES: p = board.piece_at(sq) if p: key = f"{'White' if p.color else 'Black'} {names[p.piece_type]}" pieces.setdefault(key, []).append(chess.square_name(sq)) order = [f"{c} {t}" for c in ("White", "Black") for t in ("King", "Queen", "Rook", "Bishop", "Knight", "Pawn")] arrangement = ", ".join(f"{k}: {sorted(pieces[k])}" for k in order if k in pieces) legal = ", ".join(m.uci() for m in board.legal_moves) return ( f"You are given a chess position in FEN: {fen}.\n" f"Piece positions: {arrangement}\n" f"Legal moves: {legal}\n" "Find the best move for the side to play.\n" "Analyze step by step and explain your reasoning.\n" "Finish with a single line formatted EXACTLY as:\n" "FINAL_ANSWER: <answer>\n" "Use UCI notation (e.g., e2e4, c2b1q) for the final answer." ) MODEL_ID = "UofTCSSLab/C1-4B"FEN = "2kr3r/ppp2Npp/2nbp3/6N1/2PP2n1/4B2q/PP2BP2/R2Q1RK1 b - - 2 15"messages = [{"role": "user", "content": build_prompt(FEN)}]python
# pip install transformers torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="bfloat16", device_map="auto") ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=1024, do_sample=False) # greedyprint(tok.decode(out[0, ids.shape[-1]:], skip_special_tokens=True))# ... step-by-step reasoning ...# FINAL_ANSWER: h3h2python
# pip install vllmfrom vllm import LLM, SamplingParams llm = LLM(model=MODEL_ID, dtype="bfloat16")sampling = SamplingParams(temperature=0.0, max_tokens=1024) # greedyout = llm.chat(messages, sampling_params=sampling)print(out[0].outputs[0].text)# ... step-by-step reasoning ...# FINAL_ANSWER: h3h2bibtex
@article{tang2026grounded, title={Grounded Chess Reasoning in Language Models via Master Distillation}, author={Tang, Zhenwei and Wen, Qianfeng and Grief-Albert, Seth and Elgabra, Yahya and Yang, Blair and Dong, Honghua and Anderson, Ashton}, journal={arXiv preprint arXiv:2603.20510}, year={2026}}# pip install chessimport chess def build_prompt(fen: str) -> str: board = chess.Board(fen) names = {1: "Pawn", 2: "Knight", 3: "Bishop", 4: "Rook", 5: "Queen", 6: "King"} pieces = {} for sq in chess.SQUARES: p = board.piece_at(sq) if p: key = f"{'White' if p.color else 'Black'} {names[p.piece_type]}" pieces.setdefault(key, []).append(chess.square_name(sq)) order = [f"{c} {t}" for c in ("White", "Black") for t in ("King", "Queen", "Rook", "Bishop", "Knight", "Pawn")] arrangement = ", ".join(f"{k}: {sorted(pieces[k])}" for k in order if k in pieces) legal = ", ".join(m.uci() for m in board.legal_moves) return ( f"You are given a chess position in FEN: {fen}.\n" f"Piece positions: {arrangement}\n" f"Legal moves: {legal}\n" "Find the best move for the side to play.\n" "Analyze step by step and explain your reasoning.\n" "Finish with a single line formatted EXACTLY as:\n" "FINAL_ANSWER: <answer>\n" "Use UCI notation (e.g., e2e4, c2b1q) for the final answer." ) MODEL_ID = "UofTCSSLab/C1-4B"FEN = "2kr3r/ppp2Npp/2nbp3/6N1/2PP2n1/4B2q/PP2BP2/R2Q1RK1 b - - 2 15"messages = [{"role": "user", "content": build_prompt(FEN)}]# pip install transformers torchfrom transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="bfloat16", device_map="auto") ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=1024, do_sample=False) # greedyprint(tok.decode(out[0, ids.shape[-1]:], skip_special_tokens=True))# ... step-by-step reasoning ...# FINAL_ANSWER: h3h2# pip install vllmfrom vllm import LLM, SamplingParams llm = LLM(model=MODEL_ID, dtype="bfloat16")sampling = SamplingParams(temperature=0.0, max_tokens=1024) # greedyout = llm.chat(messages, sampling_params=sampling)print(out[0].outputs[0].text)# ... step-by-step reasoning ...# FINAL_ANSWER: h3h2@article{tang2026grounded, title={Grounded Chess Reasoning in Language Models via Master Distillation}, author={Tang, Zhenwei and Wen, Qianfeng and Grief-Albert, Seth and Elgabra, Yahya and Yang, Blair and Dong, Honghua and Anderson, Ashton}, journal={arXiv preprint arXiv:2603.20510}, year={2026}}