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AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
Long-horizon LLM agents are typically trained with sparse outcome rewards, making trajectory-level objectives too coarse to distinguish the contribution of individual decisions. Step-level credit assignment provides finer-grained supervision, but its estimates can be unreliable because observed retu
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