843 citations · 1.6k across the 12 of their papers we have counts for
8 papers · 1 filter
Training Language Models to Self-Correct via Reinforcement Learning
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal +15
Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for t…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Shariq Iqbal, Robby Costales, Fei Sha
Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solvin…
Possibility Before Utility: Learning And Using Hierarchical Affordances
Robby Costales, Shariq Iqbal, Fei Sha
Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of…
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Shariq Iqbal, Christian A. Schroeder de Witt, Bei Peng +3
Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge amo…
When MAML Can Adapt Fast and How to Assist When It Cannot
Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha
Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in meta-learning tasks on many datasets and settings. On the other hand, we have just started to understa…
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
Shariq Iqbal, Fei Sha
Solving tasks with sparse rewards is one of the most important challenges in reinforcement learning. In the single-agent setting, this challenge is addressed by introducing intrins…
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Shariq Iqbal, Fei Sha
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-cri…