用完整棋局训练大模型，让其达到专业棋手水平
Complete Chess Games Enable LLM Become A Chess Master
- 将国际象棋转换为文本格式，用标注最佳走法的对局数据微调模型
- 在允许10次采样的条件下，模型达1788分专业级棋力
- 长回合对局数据比短回合数据提升350分，证明数据质量关键
大型语言模型（LLM）在文本生成、问答、翻译和推理等任务中表现出色，正迅速发展并影响科技、商业、教育与娱乐等领域。尽管如此，其在抽象类游戏（如国际象棋）中的能力仍研究不足。国际象棋要求模型从文本输入中输出合法且合理的走法。本文提出基于文本的国际象棋大模型ChessLLM，将完整对局转化为文本形式，以标准走法（Forsyth-Edwards Notation）作为标注。仅通过监督微调，模型在允许10次采样时，对阵标准评分的Stockfish棋手，达到了1788分的高水平职业级棋力。进一步表明，数据质量至关重要：长回合对局监督使模型性能相比短回合数据提升了350分的等级。
原文摘要 · Abstract (English)
Large language models (LLM) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. It continues to advance rapidly and is becoming increasingly influential in various fields, from technology and business to education and entertainment. Despite LLM's success in multiple areas, its ability to play abstract games, such as chess, is underexplored. Chess-playing requires the language models to output legal and reasonable moves from textual inputs. Here, we propose the Large language model ChessLLM to play full chess games. We transform the game into a textual format with the best move represented in the Forsyth-Edwards Notation. We show that by simply supervised fine-tuning, our model has achieved a professional-level Elo rating of 1788 in matches against the standard Elo-rated Stockfish when permitted to sample 10 times. We further show that data quality is important. Long-round data supervision enjoys a 350 Elo rating improvement over short-round data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。