GPDPO: Learning from Reward Conflicts via Group Pareto-Dominance Policy Optimization

Multi-reward reinforcement learning offers a promising framework for aligning large language models (LLMs) with diverse objectives, but conflicting reward signals can induce ambiguous update directions and hinder stable optimization. Existing approaches address such conflicts through reward reweight