1 code implementation • 10 May 2024 • Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal
Aligning Large Language Models (LLMs) to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem.
1 code implementation • 4 Apr 2024 • Lars Ankile, Anthony Simeonov, Idan Shenfeld, Pulkit Agrawal
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requiring precise, long-horizon manipulation.
1 code implementation • 29 Feb 2024 • Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James Glass, Akash Srivastava, Pulkit Agrawal
To probe when an LLM generates unwanted content, the current paradigm is to recruit a \textit{red team} of human testers to design input prompts (i. e., test cases) that elicit undesirable responses from LLMs.
no code implementations • 6 Jul 2023 • Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
To combine the benefits of these different forms of learning, it is common to train a policy to maximize a combination of reinforcement and teacher-student learning objectives.
1 code implementation • NeurIPS 2021 • Ron Dorfman, Idan Shenfeld, Aviv Tamar
Consider the following instance of the Offline Meta Reinforcement Learning (OMRL) problem: given the complete training logs of $N$ conventional RL agents, trained on $N$ different tasks, design a meta-agent that can quickly maximize reward in a new, unseen task from the same task distribution.
1 code implementation • NeurIPS 2021 • Ron Dorfman, Idan Shenfeld, Aviv Tamar
Consider the following instance of the Offline Meta Reinforcement Learning (OMRL) problem: given the complete training logs of $N$ conventional RL agents, trained on $N$ different tasks, design a meta-agent that can quickly maximize reward in a new, unseen task from the same task distribution.