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News/T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning
arxiv
PublishedOctober 3, 2026 at 4:00 AM
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T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning

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arXiv:2610.00388v1 Announce Type: new Abstract: 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 i

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