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arXiv Machine Learning By Peizheng Guo, Jianqi Zhang, Xingyu Zhang, Yun Fan, Jiahuan Zhou, Changwen Zheng, Wenwen Qiang

GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models

Read the original on arXiv Machine Learning →

GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models proposes a new method for fine‑tuning LLMs after training. The approach models each group gradient as a random variable, estimates its probability distribution, and uses Dirichlet‑based gradient uncertainty to weight each group’s contribution during policy updates. Experiments on multiple benchmarks show that this uncertainty‑aware aggregation improves the effectiveness of post‑training policy optimization.

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