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.
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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.