arXiv: cs.AI preprintsInternational6 November 2026
AttnSR: Attention Step Reward for Long-Horizon Agentic RL
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arXiv:2611.07932v1 Announce Type: cross Abstract: Long-horizon LLM agents make multi-step decisions, yet outcome-reward-only reinforcement learning provides little direct supervision for individual steps. We introduce AttnSR, a step-level reward augmentation method that derives reward allocations from policy attention extracted during training replay, without a separately trained reward model or additional rollouts. AttnSR traces backward through the policy model attention graph to estimate the relevance of earlier steps to the final response. The terminal step, measured relative to a running
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