AI RESEARCH
From $\boldsymbol{\log\pi}$ to $\boldsymbol{\pi}$: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight
arXiv CS.AI
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ArXi:2603.14389v1 Announce Type: cross Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics remain fragile. Standard algorithms like GRPO enforce stability via ``hard clipping'', which inadvertently stifles exploration by discarding gradients of tokens outside the trust region.