Spreadsheet-RL: Advancing LLM Agents on Realistic Spreadsheet Tasks
The article introduces Spreadsheet-RL, a framework designed to enhance AI agents' capabilities in handling spreadsheet tasks through reinforcement learning. It addresses the limitations of existing spreadsheet agents that struggle with complex workflows. The framework shows significant improvements in performance on both general and domain-specific spreadsheet tasks, indicating its potential for real-world applications.
- ▪Spreadsheet systems are crucial in modern data-centric workflows.
- ▪Spreadsheet-RL utilizes reinforcement learning to train specialized spreadsheet agents.
- ▪The framework includes a new Domain-Spreadsheet benchmark dataset and a Spreadsheet Gym environment.
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Computer Science > Artificial Intelligence arXiv:2605.22642 (cs) [Submitted on 21 May 2026] Title:Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning Authors:Banghao Chi, Yining Xie, Mingyuan Wu, Jingcheng Yang, Jize Jiang, Zhaoheng Li, Shengyi Qian, Minjia Zhang, Klara Nahrstedt, Rui Hou, Xiangjun Fan, Hanchao Yu View a PDF of the paper titled Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning, by Banghao Chi and 11 other authors View PDF HTML (experimental) Abstract:Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows.
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