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.
Rl-pruner: Struc- tured pruning using reinforcement learning for cnn com- pression and acceleration.arXiv preprint arXiv:2411.06463
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LTS-FS locates hallucination-relevant layers in LVLMs via causal attribution on a constructed dataset and applies sparse layerwise feature steering to mitigate hallucinations while preserving general task performance.
citing papers explorer
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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.
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Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation
LTS-FS locates hallucination-relevant layers in LVLMs via causal attribution on a constructed dataset and applies sparse layerwise feature steering to mitigate hallucinations while preserving general task performance.