Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning

Researchers introduced Spreadsheet-RL, a reinforcement learning framework for spreadsheet-capable language model agents.

It trains agents in a realistic Excel environment and uses automated start-goal spreadsheet collection plus a new domain benchmark.

The approach delivers large accuracy gains on both general spreadsheet tasks and domain-specific workflows.

The results suggest RL can make AI spreadsheet agents more reliable for real-world office automation and data work.