ProcessLight and STeP-PO: LLM-based step-wise policy optimization for traffic signal control (arXiv:2609.22746v1)
The paper presents ProcessLight, an LLM-centered framework that decomposes traffic-signal decisions into verifiable semantic steps, and STeP-PO, a reinforcement learning method that assigns step-level advantages using step quality and importance to optimize reasoning tokens rather than only final outcomes; experiments on multiple real-world datasets reportedly show improved performance and the authors released code on GitHub.