Max Sobol Mark

I am a Ph.D. student at the Computer Science Department of Carnegie Mellon University, advised by Aviral Kumar. Previously, I obtained my B.S. and M.S. in Computer Science from Stanford University, where I was advised by Chelsea Finn.

I am working on Reinforcement Learning algorithms that can leverage large datasets and large models to learn new skills extremely fast, particularly for robotics.

Email | Resume | Scholar | Github

Research

	Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone Max Sobol Mark, Tian Gao, Georgia Gabriela Sampaio, Mohan Kumar, Archit Sharma, Chelsea Finn, Aviral Kumar project page / code / arXiv
	Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning Jingyun Yang, Max Sobol Mark, Brandon Vu, Archit Sharma, Jeannette Bohg, Chelsea Finn ICRA, 2023 project page / video / arXiv
	Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn arXiv / code
	Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine NeurIPS, 2023 project page / video / arXiv / code
	Fine-tuning Offline Policies with Optimistic Action Selection Max Sobol Mark, Ali Ghadirzadeh, Xi Chen, Chelsea Finn NeurIPS DeepRL Workshop, 2022 Paper
	Unsupervised Learning from Video with Deep Neural Embeddings Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins CVPR, 2020 Paper / Code

Teaching

CS330: Deep Multi-Task and Meta Learning
Course Assistant, Stanford University, Fall 2022 and 2023

CS234: Reinforcement Learning
Course Assistant, Stanford University, Winter 2023

CS224R: Deep Reinforcement Learning
Course Assistant, Stanford University, Spring 2023

Last updated: 2024-12-12. Website template.