RL for Tool Use

This field studies how language and multimodal agents can use tools such as search, code execution, databases, APIs, and graphical interfaces to solve multi-step tasks. Common approaches combine supervised demonstrations with policy optimization, often using executable environments and rewards for task success, valid calls, and efficient execution. A major focus is improving credit assignment across long interactions through turn-level rewards, process supervision, and experience or trajectory reuse. Open challenges include preventing tool overuse and training collapse while ensuring that skills learned in simulated or cached environments transfer reliably to changing real-world tools.