790 citations · 2.7k across the 58 of their papers we have counts for
10 papers · 1 filter
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
Zhiyuan Zhou, Andy Peng, Qiyang Li +2
The modern paradigm in machine learning involves pre-training on diverse data, followed by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning vi…
Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar +1
Large, general-purpose robotic policies trained on diverse demonstration datasets have been shown to be remarkably effective both for controlling a variety of robots in a range of…
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
Hao Bai, Yifei Zhou, Mert Cemri +4
Training corpuses for vision language models (VLMs) typically lack sufficient amounts of decision-centric data. This renders off-the-shelf VLMs sub-optimal for decision-making task…
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar, Anikait Singh, Archit Sharma +6
Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised le…
Is Value Learning Really the Main Bottleneck in Offline RL?
Seohong Park, Kevin Frans, Sergey Levine +1
While imitation learning requires access to high-quality data, offline reinforcement learning (RL) should, in principle, perform similarly or better with substantially lower data q…