Clipping-Free Policy Optimization for Large Language Models

Ömer Veysel Çağatan, Barış Akgün, Gözde Gül Şahin, Xuandong Zhao

Clipping-Free Policy Optimization for Large Language Models: 3 upvotes on Hugging Face Daily Papers, #53 of 73 papers on 2026-02-03. Day-by-day upvote history.

Reinforcement learning has become central to post-training large language models, yet dominant algorithms rely on clipping mechanisms that introduce optimization issues at scale, including zero-gradient regions, reward hacking, and training instability. We propose Clipping-Free Policy Optimization (CFPO), which replaces heuristic clipping with a convex quadratic penalty derived from Total Variation divergence constraints, yielding an everywhere-differentiable objective that enforces stable policy updates without hard boundaries. We evaluate CFPO across both reasoning and alignment settings. In reasoning, CFPO matches clipping-based methods on downstream benchmarks while extending the stable training regime. In alignment, CFPO mitigates verbosity exploitation and reduces capability degradation, while achieving competitive instruction-following performance. CFPO requires only a one-line code change and no additional hyperparameters. Our results suggest that CFPO is a promising drop-in alternative to clipping-based methods for LLM post-training.

Paper page on Hugging Face · arXiv

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