On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation problem. However, deploying capable LLMs in resource-constrained onboard systems remains a fundamental challenge. In this paper, we study how to effectively transfer motion planning knowledge from a large teacher LLM to a smaller, more deployable student model. We build on the GPT-Driver framework, which represents driving scenes as language prompts and generates waypoint trajectories with chain-of-thought reasoning, and investigate two student training paradigms: (i) on-policy generalized knowledge distillation (GK...
| Authors | Amirhossein Afsharrad, Amirhesam Abedsoltan, Ahmadreza Moradipari et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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