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ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning

Single source1 source14 Oct 2026

arXiv:2610.18683v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO) suffer from a fundamental uniform credit assig

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arxiv.orghttps://arxiv.org/abs/2610.18683

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