Yingtie Lei

LLM agents struggle to generalize from experience to reusable skills, often performing worse than simply replaying past trajectories, revealing a critical gap in current abstraction methods.

VLA models can now be efficiently quantized *without* retraining, even surpassing full-precision performance, thanks to a new post-training method that carefully calibrates scales across attention and output heads.