ProcessLight: Process Supervision for Large Language Model Based Traffic Signal Control
Large Language Models (LLMs) have recently been introduced into traffic signal control (TSC) as decision agents due to their strengths in human-readable reasoning generation. Yet, existing LLM TSC methods optimize only from final outcomes and fail to distinguish valid from flawed reasoning steps, causing useful or misleading steps to be jointly updated and thus impairing the model's learning of effective reasoning. To bridge this gap, we propose an LLM-based framework ProcessLight to decompose signal decisions into verifiable semantic steps. Building on ProcessLight, we further develop Step-wise Traffic Process Policy Optimization (STeP-PO), a novel reinforcement learning framework that o...
| Authors | Huaitao Zhao, Tianlong Zhou, Weijie Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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