Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving (arxiv.org)

Researchers evaluated whether core large language model (LLM) components can be reused for motion generation in autonomous driving, a domain that shares autoregressive sequence modeling, token-based representations, and context-aware decision making with NLP. They performed a systematic study of five LLM modules—tokenizer design, positional embedding, pre-training paradigms, post-training strategies, and test-time computation—on the Waymo Sim Agents benchmark. The paper shows that with appropriate adaptation these modules can materially improve motion-generation performance and reports competitive results on the Sim Agents task. Technically, the work maps discrete-token machinery and sequence priors from LLMs onto trajectory prediction/control, assesses which module designs survive the shift from text to continuous, spatial-temporal data, and diagnoses why some choices fail without modification. The findings highlight concrete adaptation needs (e.g., task-aware tokenization and position encoding, domain-relevant pretraining objectives, and post-training/test-time strategies tailored to real-world dynamics) and offer guidance on when reuse is effective versus when new designs are necessary. For the AI/ML community, the study provides a practical blueprint for transferring LLM building blocks into embodied, continuous domains and clarifies limits and opportunities for cross-domain model reuse.