790 citations · 2.7k across the 58 of their papers we have counts for
59 papers
Adaptation of Generalist Robot Policies with Minimal Data
Shreyas Kowshik, Sreyas Venkataraman, Leo Wang +3
A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning rema…
Recursive Agent Optimization
Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang +2
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new instantiations…
RLAC: Reinforcement Learning with Adversarial Critic for Free-Form Generation Tasks
Mian Wu, Gavin Zhang, Sewon Min +2
Open-ended generation tasks require outputs to satisfy diverse and often implicit task-specific evaluation rubrics. The sheer number of relevant rubrics leads to prohibitively high…
Horizon Reduction Makes RL Scalable
Seohong Park, Kevin Frans, Deepinder Mann +3
In this work, we study the scalability of offline reinforcement learning (RL) algorithms. In principle, a truly scalable offline RL algorithm should be able to solve any given prob…
Scaling Test-Time Compute Without Verification or RL is Suboptimal
Amrith Setlur, Nived Rajaraman, Sergey Levine +1
Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued and efficient improvements with scal…
Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
Hao Bai, Yifei Zhou, Li Erran Li +2
While a number of existing approaches for building foundation model agents rely on prompting or fine-tuning with human demonstrations, it is not sufficient in dynamic environments…