Preprint Open access
T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning
Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, rewards in many interactive tasks reflect only the final outcome, providing limited guidance on which intermediate decisions advance the task. Successful training trajecto …