OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering

Zipeng Qiu

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering

Computation and Language 2025-07-08 v1

Authors: Zipeng Qiu

Abstract

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.

Keywords

reinforcement learning multi-agent systems relational database

Cite

@article{arxiv.2507.03018,
  title  = {OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering},
  author = {Zipeng Qiu},
  journal= {arXiv preprint arXiv:2507.03018},
  year   = {2025}
}

OpenTable-R1: A Reinforcement Learning Augmented Tool Agent for Open-Domain Table Question Answering

Abstract

Keywords

Cite

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