257 citations · 1k across the 96 of their papers we have counts for
16 papers · 1 filter
Multi-agent Coordination via Flow Matching
Dongsu Lee, Daehee Lee, Amy Zhang
This work presents MAC-Flow, a simple yet expressive framework for multi-agent coordination. We argue that requirements of effective coordination are twofold: (i) a rich representa…
Unifying Agent Interaction and World Information for Multi-agent Coordination
Dongsu Lee, Daehee Lee, Yaru Niu +3
This work presents a novel representation learning framework, *interaction-world* latent (IWoL), to facilitate *team coordination* in multi-agent reinforcement learning (MARL). Bui…
Correct Reasoning Paths Visit Shared Decision Pivots
Dongkyu Cho, Amy B. Z. Zhang, Bilel Fehri +4
Chain-of-thought (CoT) reasoning exposes the intermediate thinking process of large language models (LLMs), yet verifying those traces at scale remains unsolved. In response, we in…
Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
Viraj Joshi, Zifan Xu, Bo Liu +2
Multi-task Reinforcement Learning (MTRL) has emerged as a critical training paradigm for applying reinforcement learning (RL) to a set of complex real-world robotic tasks, which de…
ExPO: Unlocking Hard Reasoning with Self-Explanation-Guided Reinforcement Learning
Ruiyang Zhou, Shuozhe Li, Amy Zhang +1
Self-improvement via RL often fails on complex reasoning tasks because GRPO-style post-training methods rely on the model's initial ability to generate positive samples. Without gu…
Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation
Sangmin Jung, Utkarsh Nath, Yezhou Yang +5
Text-to-image generation models have achieved remarkable capabilities in synthesizing images, but often struggle to provide fine-grained control over the output. Existing guidance…
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Caleb Chuck, Fan Feng, Carl Qi +4
Hindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL), especially in certain domains such as navigation and locomotion.…
Multimodal Language Models as Text-to-Image Model Evaluators
Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat +5
The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to…
An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning
Haoran Xu, Shuozhe Li, Harshit Sikchi +2
We introduce Iterative Dual Reinforcement Learning (IDRL), a new method that takes an optimal discriminator-weighted imitation view of solving RL. Our method is motivated by a simp…
Fast Adaptation with Behavioral Foundation Models
Harshit Sikchi, Andrea Tirinzoni, Ahmed Touati +6
Unsupervised zero-shot reinforcement learning (RL) has emerged as a powerful paradigm for pretraining behavioral foundation models (BFMs), enabling agents to solve a wide range of…
Information-Theoretic Reward Decomposition for Generalizable RLHF
Liyuan Mao, Haoran Xu, Amy Zhang +2
A generalizable reward model is crucial in Reinforcement Learning from Human Feedback (RLHF) as it enables correctly evaluating unseen prompt-response pairs. However, existing rewa…
Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets
Alexander Levine, Peter Stone, Amy Zhang
While sequential decision-making environments often involve high-dimensional observations, not all features of these observations are relevant for control. In particular, the obser…
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Arthur Zhang, Harshit Sikchi, Amy Zhang +1
We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to a…
Augmented Conditioning Is Enough For Effective Training Image Generation
Jiahui Chen, Amy Zhang, Adriana Romero-Soriano
Image generation abilities of text-to-image diffusion models have significantly advanced, yielding highly photo-realistic images from descriptive text and increasing the viability…
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
Towards General-Purpose Model-Free Reinforcement Learning
Scott Fujimoto, Pierluca D'Oro, Amy Zhang +2
Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefu…