Skip to main content

RL Fine-Tune for Tools

Reward the tool calls that work, and the model learns to use its tools well.

Key Insight​

Instead of only imitating recorded tool-call examples, this project trains the model with GRPO using a verifier that checks whether each tool call produced the expected output — a form of RLVR applied to tool use.

Why This Matters​

A verifiable success signal lets the model practice using its tools and learn from what actually works, rather than just copying traces — the frontier recipe for reliable tool-using agents.