The paper proposes a principled reward design framework for training LLMs on tool use via reinforcement learning, leading to significant gains over SFT and baseline models in generalization and performance.

We propose RF-Agent, an automated RL reward function design framework via language agent tree search.

We construct a Progress Reward Model with convergence guarantee for Reinforcement Learning via Large Language Models.