356 citations · 2k across the 84 of their papers we have counts for
51 papers · 1 filter
Visual Pre-Training on Unlabeled Images using Reinforcement Learning
Dibya Ghosh, Sergey Levine
In reinforcement learning (RL), value-based algorithms learn to associate each observation with the states and rewards that are likely to be reached from it. We observe that many s…
Diffusion Guidance Is a Controllable Policy Improvement Operator
Kevin Frans, Seohong Park, Pieter Abbeel +1
At the core of reinforcement learning is the idea of learning beyond the performance in the data. However, scaling such systems has proven notoriously tricky. In contrast, techniqu…
: a Vision-Language-Action Model with Open-World Generalization
Physical Intelligence, Kevin Black, Noah Brown +33
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated im…
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data
Nitish Dashora, Dibya Ghosh, Sergey Levine
Online reinforcement learning (RL) with sparse rewards poses a challenge partly because of the lack of feedback on states leading to the goal. Furthermore, expert offline data with…
SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks
Yifei Zhou, Song Jiang, Yuandong Tian +4
Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform…
Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents
Yifei Zhou, Qianlan Yang, Kaixiang Lin +5
The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advance…