用强化学习让小模型直接操作工具查表，准确率从个位数飙到86%以上。
OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering
- 把搜索和数据库查询当工具，让大模型直接调用完成问答
- 40亿参数小模型经两阶段训练，零样本下准确率超86%
- 适合想用小模型做复杂表格问答的开发者
开放域表格问答传统上采用静态检索加封闭域回答的两阶段流程。本文提出一种端到端智能体框架，将多轮工具调用（基于BM25+的搜索API和SQLite SQL执行器）直接嵌入大语言模型中。为适配紧凑的40亿参数模型，设计两阶段微调：先在简单问题上进行监督冷启动，再通过异步GRPO强化学习在更难问题上优化，使用LoRA适配器和回放缓冲区。该统一方法使模型能协同检索、推理与执行查询，在保留测试集上实现零样本精确匹配率超过0.86，显著提升。结果表明，结合结构化工具调用与针对性强化学习微调，可实现可扩展、高精度的表格问答。代码已开源。
原文摘要 · Abstract (English)
Open-domain table question answering traditionally relies on a two-stage pipeline: static table retrieval followed by a closed-domain answer. In contrast, we propose an end-to-end agentic framework that embeds multi-turn tool calls-using a BM25+-based search API and a SQLite SQL executor-directly into a large language model. To further adapt a compact 4B-parameter model, we introduce a two-stage fine-tuning process: supervised cold-start on easy questions, then Async GRPO reinforcement learning on harder cases with LoRA adapters and a rollout buffer. This unified approach enables the model to jointly retrieve, reason, and execute queries, yielding a dramatic accuracy improvement from single-digit zero-shot performance to over 0.86 exact match on a held-out test set. Our results underscore the effectiveness of integrating structured tool calls with targeted RL fine-tuning for scalable, accurate table QA. The code is available at https://github.com/TabibitoQZP/OpenTableR1.
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