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Northwestern University
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MoRE achieves a staggering 44 percentage point increase in deployment success rates by seamlessly integrating behavior mode redirection into policy weights, eliminating the need for inference-time adjustments.
Current VLMs miss crucial transition-level physics, but APT-Tune teaches them to learn causal transitions without forgetting event-level context.
Transforming failures into focused training tasks boosts tool-using language model performance by over 8% on key benchmarks.
VLMs can learn to actively reason and plan in 3D environments by distilling view graphs from self-exploration trajectories, enabling them to surpass even larger models like GPT-4 Pro and Gemini 1.5 Pro on interactive view planning.
