7 citations · 9 across the 19 of their papers we have counts for
10 papers · 1 filter
Decoupled Q-Chunking
Qiyang Li, Seohong Park, Sergey Levine
Temporal-difference (TD) methods learn state and action values efficiently by bootstrapping from their own future value predictions, but such a self-bootstrapping mechanism is pron…
Scalable Offline Model-Based RL with Action Chunks
Kwanyoung Park, Seohong Park, Youngwoon Lee +1
In this paper, we study whether model-based reinforcement learning (RL), in particular model-based value expansion, can provide a scalable recipe for tackling complex, long-horizon…
Leveraging Discrete Function Decomposability for Scientific Design
James C. Bowden, Sergey Levine, Jennifer Listgarten
In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a pro…
Transitive RL: Value Learning via Divide and Conquer
Seohong Park, Aditya Oberai, Pranav Atreya +1
In this work, we present Transitive Reinforcement Learning (TRL), a new value learning algorithm based on a divide-and-conquer paradigm. TRL is designed for offline goal-conditione…
Dual Goal Representations
Seohong Park, Deepinder Mann, Sergey Levine
In this work, we introduce dual goal representations for goal-conditioned reinforcement learning (GCRL). A dual goal representation characterizes a state by "the set of temporal di…
Steering Your Diffusion Policy with Latent Space Reinforcement Learning
Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang +5
Robotic control policies learned from human demonstrations have achieved impressive results in many real-world applications. However, in scenarios where initial performance is not…
Intention-Conditioned Flow Occupancy Models
Chongyi Zheng, Seohong Park, Sergey Levine +1
Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the comm…
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…
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…
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang +6
Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization…