Spreadsheet-RL: RL Fine-Tuning for LLM Agents on Spreadsheet Tasks
A new framework called Spreadsheet-RL has been developed by researchers to enhance large language model agents through reinforcement learning, enabling them to tackle intricate, multi-step tasks within authentic Microsoft Excel settings. This framework features an automated system designed for the efficient gathering of paired start-goal spreadsheets sourced from online communities, alongside specialized evaluation tasks in finance and other fields. Current spreadsheet agents depend on specific prompting techniques with general-purpose LLMs, which often falter in real-world applications. The findings are detailed in the arXiv paper numbered 2605.22642.
Key facts
- Spreadsheet-RL is a reinforcement learning fine-tuning framework for spreadsheet agents.
- It operates within a realistic Microsoft Excel environment.
- Features an automated pipeline for collecting paired start-goal spreadsheets from online forums.
- Includes domain-specific evaluation tasks in finance and other areas.
- Existing agents rely on specialized prompting over general-purpose LLMs.
- The framework aims to handle complex, multi-step workflows.
- The paper is available on arXiv with ID 2605.22642.
- Spreadsheet systems like Excel and Google Sheets are central to data-centric workflows.
Entities
Institutions
- Microsoft
- arXiv