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LLM-controlled robots can be made significantly safer by filtering unsafe natural language commands *before* they're executed, preventing downstream errors.
Quantizing large vision-language models just got a whole lot better: a new token-level sensitivity metric closes the accuracy gap with full-precision models by up to 1.6% in 3-bit weight-only quantization.
PRIMT tackles the data inefficiency of preference-based RL by using foundation models to generate synthetic multimodal feedback and synthesize trajectories, significantly outperforming existing FM-based and scripted baselines.
