multi-reward

GD^2PO introduces a conflict-aware filtering mechanism to mitigate multi-reward conflicts in reinforcement learning for large language models, preventing signal cancellation and accelerating training efficiency.

DVAO adaptively weights objectives based on reward variance to improve multi-reward RL training stability and multi-objective performance.

UniDoc-RL presents a reinforcement learning framework for Large Vision-Language Models that optimizes retrieval, reranking, and visual reasoning through hierarchical decision-making and dense multi-reward supervision, achieving up to 17.7% improvements over prior RL-based methods on visual RAG tasks.