GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

Reinforcement learning has become a widely used post-training approach for LLM agents, where training commonly relies on outcome-level rewards that provide only coarse supervision. While finer-grained credit assignment is promising for effective policy updates, obtaining reliable local credit and assigning it to the right parts of the long-horizon trajectory remains an open challenge. In this paper, we propose Granularity-adaptivE Advantage Reweighting (GEAR), an adaptive-granularity credit assi

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ethics.ai (12 May 2026), “GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation,” evidence record 4483, https://ethics.ai/record/4483 (originally published by arXiv).

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