Clipping-Free Policy Optimization for Large Language Models

• URL: http://arxiv.org/abs/2601.22801v1
• Date: Fri, 30 Jan 2026 10:32:37 GMT
• Title: Clipping-Free Policy Optimization for Large Language Models
• Authors: Ömer Veysel Çağatan, Barış Akgün, Gözde Gül Şahin, Xuandong Zhao,
• Abstract summary: Reinforcement learning has become central to post-training large language models.<n> dominant algorithms rely on clipping mechanisms to introduce optimization issues at scale.<n>We propose Clipping-Free Policy Optimization, which replaces clipping with a convex penalty derived from Total Variation divergence constraints.
• Score: 30.663054788473598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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