273 citations · 275 across the 4 of their papers we have counts for
4 papers
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Rohin Manvi, Anikait Singh, Stefano Ermon
Inference-time computation is a powerful paradigm to enhance the performance of large language models (LLMs), with Best-of-N sampling being a widely used technique. However, this m…
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…
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Chethan Bhateja, Derek Guo, Dibya Ghosh +6
Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinfo…
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Anthony Brohan, Noah Brown, Justice Carbajal +51
We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic…