Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration 事件

Distilling LLM Reasoning into an Interpretable Policy Tree for Human-AI Collaboration arXiv:2606.08596v1 Announce Type: new Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at