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Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning
Reinforcement learning is now the standard way to train large language model agents on long-horizon tasks, where dozens of interdependent actions precede a single sparse reward. Critic-free, group-relative methods such as GRPO suit this regime, but they broadcast one trajectory-level scalar to every
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