Large language models (LLMs) typically cannot adapt their weights dynamically to new tasks or knowledge. The Self-Adapting LLMs (SEAL) framework addresses this limitation by allowing models to generate their own finetuning data and directives for self-adaptation through a reinforcement learning approach, resulting in persistent weight updates and improved performance in knowledge incorporation and few-shot generalization tasks.

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.