Tootfinder

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning
Rahel Rickenbach, Bruce Lee, Ren\'e Zurbr\"ugg, Carmen Amo Alonso, Melanie N. Zeilinger
https://arxiv.org/abs/2507.12855

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning
The integration of large language models (LLMs) with control systems has demonstrated significant potential in various settings, such as task completion with a robotic manipulator. A main reason for this success is the ability of LLMs to perform in-context learning, which, however, strongly relies on the design of task examples, closely related to the target tasks. Consequently, employing LLMs to formulate optimal control problems often requires task examples that contain explicit mathematical …

"If you always put limits on everything you do, physical or anything else. It will spread into your work and into your life. There are no limits. There are only plateaus, and you must not stay there, you must go beyond them."
-Bruce Lee
#acting #coaching

Distillation Robustifies Unlearning
Bruce W. Lee, Addie Foote, Alex Infanger, Leni Shor, Harish Kamath, Jacob Goldman-Wetzler, Bryce Woodworth, Alex Cloud, Alexander Matt Turner
https://arxiv.org/abs/2506.06278

Distillation Robustifies Unlearning
Current LLM unlearning methods are not robust: they can be reverted easily with a few steps of finetuning. This is true even for the idealized unlearning method of training to imitate an oracle model that was never exposed to unwanted information, suggesting that output-based finetuning is insufficient to achieve robust unlearning. In a similar vein, we find that training a randomly initialized student to imitate an unlearned model transfers desired behaviors while leaving undesired capabilitie…