DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning 事件

DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning arXiv:2605.25604v1 Announce Type: new Abstract: Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative Policy Optimization offers an efficient, value-model-free alternative to Proximal Policy Optimization, adapting it to real-world multi-reward settings remains challenging. Standard scalarization practices,