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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. Successf
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