Tool-Use RL

Train language models to autonomously decide when and how to use external tools (APIs, calculators, search, code interpreters) through reinforcement learning from tool interaction outcomes. Tool-use RL trains models to generate tool calls (function invocations) in addition to text. The model outputs structured tool calls, executes them, receives the result, and continues generating with the tool output in context. Training uses outcome-based rewards (did the tool use help solve the task?), which naturally handles the exploration-exploitation tradeoff of tool selection. The training loop interleaves text generation with tool execution, and the policy gradient rewards successful tool-use trajectories.