294 citations · 426 across the 27 of their papers we have counts for
8 papers · 1 filter
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
Annie S. Chen, Govind Chada, Laura Smith +4
To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios duri…
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
Jingyun Yang, Max Sobol Mark, Brandon Vu +3
The pre-train and fine-tune paradigm in machine learning has had dramatic success in a wide range of domains because the use of existing data or pre-trained models on the internet…
An Emulator for Fine-Tuning Large Language Models using Small Language Models
Eric Mitchell, Rafael Rafailov, Archit Sharma +2
Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-t…
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Max Sobol Mark, Archit Sharma, Fahim Tajwar +3
It is desirable for policies to optimistically explore new states and behaviors during online reinforcement learning (RL) or fine-tuning, especially when prior offline data does no…
Waypoint-Based Imitation Learning for Robotic Manipulation
Lucy Xiaoyang Shi, Archit Sharma, Tony Z. Zhao +1
While imitation learning methods have seen a resurgent interest for robotic manipulation, the well-known problem of compounding errors continues to afflict behavioral cloning (BC).…
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafael Rafailov, Archit Sharma, Eric Mitchell +3
While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the comp…
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
Katherine Tian, Eric Mitchell, Allan Zhou +5
A trustworthy real-world prediction system should produce well-calibrated confidence scores; that is, its confidence in an answer should be indicative of the likelihood that the an…
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
Archit Sharma, Ahmed M. Ahmed, Rehaan Ahmad +1
In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. An aspirational goal is to construct self-improving…