arXiv:2608. 07151v1 Announce Type: cross Abstract: Reinforcement learning policies are difficult to inspect, but interpreting them is a prerequisite for trustworthiness.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 07235v1 Announce Type: cross Abstract: Explainability remains a key issue in reinforcement learning (RL).
arXiv:2606. 04634v1 Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior.
arXiv:2606. 08596v1 Announce Type: new Abstract: Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration.
arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).
arXiv:2606. 06976v1 Announce Type: new Abstract: Large language model (LLM)-based agents often make suboptimal tool-use decisions, including unsupported tool invocation and hallucinated direct responses, which may accumulate errors throughout multi-step interactions.
arXiv:2607. 25925v1 Announce Type: new Abstract: Over the past decade, decision trees have been used to represent controllers (a.