Distillation from frontier VLMs plus E-RLVR regularization produces a 4B local model that achieves 34.5% SR on OVON while cutting inference latency by 82.8%.
Enhancing autonomous driving systems with on-board deployed large language models
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BPF prunes embodied LLM controllers iteratively during RL (and optionally SFT) to achieve superior size-performance-throughput trade-offs compared to post-training pruning or smaller dense models on the RobotxR1 autonomous driving pipeline.
PA-LLM-RAG adds policy retrieval and dual-LLM verification to enable reliable low-latency mission orchestration in simulated IoBT environments, with Gemma-2B reaching 100% policy compliance at 4.17s latency.
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.
citing papers explorer
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LocalNav: Distilling Frontier VLMs and Embodied RL for On-Device Object Goal Navigation
Distillation from frontier VLMs plus E-RLVR regularization produces a 4B local model that achieves 34.5% SR on OVON while cutting inference latency by 82.8%.
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Before Parc Ferm\'e: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving
BPF prunes embodied LLM controllers iteratively during RL (and optionally SFT) to achieve superior size-performance-throughput trade-offs compared to post-training pruning or smaller dense models on the RobotxR1 autonomous driving pipeline.