DuPO introduces a dual learning-based preference optimization framework designed to generate annotation-free feedback, overcoming limitations of existing methods such as RLVR and traditional dual learning. By decomposing a task's input into known and unknown components and reconstructing the unknown part, DuPO enhances various tasks, achieving significant improvements in translation quality and mathematical reasoning accuracy. This framework positions itself as a scalable and general approach for optimizing large language models (LLMs) without the need for costly labels.

TreeRL is a novel reinforcement learning framework that integrates on-policy tree search to enhance the training of language models. By incorporating intermediate supervision and optimizing search efficiency, TreeRL addresses issues common in traditional reinforcement learning methods, such as distribution mismatch and reward hacking. Experimental results show that TreeRL outperforms existing methods in math and code reasoning tasks, showcasing the effectiveness of tree search in this domain.