24 citations · 40 across the 15 of their papers we have counts for
15 papers
Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
Han Li, Jinyu Tian, Rili Feng +10
Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliabil…
Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning
Aravind Venugopal, Jiayu Chen, Xudong Wu +3
The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. Generative world models captu…
UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs
Devan Shah, Owen Yang, Daniel Yang +2
Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, but standard approa…
Can We Really Learn One Representation to Optimize All Rewards?
Chongyi Zheng, Royina Karegoudra Jayanth, Benjamin Eysenbach
As unsupervised pretraining becomes increasingly ubiquitous in reinforcement learning, a more thorough theoretical understanding of these methods becomes of equal importance to the…
Consistent Zero-Shot Imitation with Contrastive Goal Inference
Kathryn Wantlin, Chongyi Zheng, Benjamin Eysenbach
Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.…
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn +2
While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to pr…
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…
Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning
Chongyi Zheng, Jens Tuyls, Joanne Peng +1
Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design…
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
Vivek Myers, Chongyi Zheng, Anca Dragan +2
Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time betw…
Contrastive Difference Predictive Coding
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representat…
Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior
Ruihan Yang, Zhuoqun Chen, Jianhan Ma +4
The agility of animals, particularly in complex activities such as running, turning, jumping, and backflipping, stands as an exemplar for robotic system design. Transferring this s…
BridgeData V2: A Dataset for Robot Learning at Scale
Homer Walke, Kevin Black, Abraham Lee +11
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096…
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Chongyi Zheng, Benjamin Eysenbach, Homer Walke +4
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strat…
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
Learning Nearly Decomposable Value Functions Via Communication Minimization
Tonghan Wang, Jianhao Wang, Chongyi Zheng +1
Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a p…