Bootstrapping Task Spaces for Self-Improvement (arxiv.org)

Researchers have introduced Exploratory Iteration (ExIt), a novel reinforcement learning framework designed to train large language models (LLMs) for effective multi-step self-improvement at inference time. Unlike traditional methods that fix a maximum iteration depth—leading to arbitrary limits and inefficiencies—ExIt leverages the recurrent structure of self-improvement tasks by selectively expanding the task space with the most informative intermediate partial solutions encountered during episodes. This bootstrapping process treats these partial histories as new tasks, enabling the model to learn robust self-improvement policies from single-step training data while generalizing to longer and more complex iteration sequences. ExIt also incorporates explicit exploration strategies to maintain diversity in the task space, enhancing the model's adaptability across different domains. The authors validate ExIt across varied challenge areas including competitive mathematics, multi-turn tool use, and machine learning engineering, showing that policies trained with this method can consistently improve their outputs beyond the training iteration depths. This advancement is significant for the AI community as it pushes the boundaries of autonomous agent learning, enabling models to iteratively refine their solutions during deployment without expensive retraining or fixed iteration constraints, thereby advancing scalable, self-improving AI systems.