742 citations · 896 across the 19 of their papers we have counts for
21 papers
Asking the Right Questions: Improving Reasoning with Generated Stepping Stones
Hengyuan Hu, Tingchen Fu, Minqi Jiang +3
Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may n…
Investigating Data Pruning for Pretraining Biological Foundation Models at Scale
Yifan Wu, Jiyue Jiang, Xichen Ye +9
Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse down…
Scalable Decision Making for Games of Imperfect Information
Samuel Sokota, Eugene Vinitsky, Hengyuan Hu +3
Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfortunately, the presence of large a…
Toward Grounded Commonsense Reasoning
Minae Kwon, Hengyuan Hu, Vivek Myers +3
Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car. A human may recognize that it is not appropriate to disassemble the sports car and put…
The Update-Equivalence Framework for Decision-Time Planning
Samuel Sokota, Gabriele Farina, David J. Wu +4
The process of revising (or constructing) a policy at execution time -- known as decision-time planning -- has been key to achieving superhuman performance in perfect-information g…
Language Instructed Reinforcement Learning for Human-AI Coordination
Hengyuan Hu, Dorsa Sadigh
One of the fundamental quests of AI is to produce agents that coordinate well with humans. This problem is challenging, especially in domains that lack high quality human behaviora…
Human-AI Coordination via Human-Regularized Search and Learning
Hengyuan Hu, David J Wu, Adam Lerer +2
We consider the problem of making AI agents that collaborate well with humans in partially observable fully cooperative environments given datasets of human behavior. Inspired by p…
K-level Reasoning for Zero-Shot Coordination in Hanabi
Brandon Cui, Hengyuan Hu, Luis Pineda +1
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP polici…
Self-Explaining Deviations for Coordination
Hengyuan Hu, Samuel Sokota, David Wu +4
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which huma…
Modeling Strong and Human-Like Gameplay with KL-Regularized Search
Athul Paul Jacob, David J. Wu, Gabriele Farina +5
We consider the task of building strong but human-like policies in multi-agent decision-making problems, given examples of human behavior. Imitation learning is effective at predic…
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos +2
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many ot…
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Hengyuan Hu, Adam Lerer, Noam Brown +1
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark…
Off-Belief Learning
Hengyuan Hu, Adam Lerer, Brandon Cui +4
The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbi…
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Jack Parker-Holder, Luke Metz, Cinjon Resnick +6
Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…
"Other-Play" for Zero-Shot Coordination
Hengyuan Hu, Adam Lerer, Alex Peysakhovich +1
We consider the problem of zero-shot coordination - constructing AI agents that can coordinate with novel partners they have not seen before (e.g. humans). Standard Multi-Agent Rei…
Polygames: Improved Zero Learning
Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen +21
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connec…
Improving Policies via Search in Cooperative Partially Observable Games
Adam Lerer, Hengyuan Hu, Jakob Foerster +1
Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just…
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Hengyuan Hu, Jakob N Foerster
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common…
Hierarchical Decision Making by Generating and Following Natural Language Instructions
Hengyuan Hu, Denis Yarats, Qucheng Gong +2
We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making. Rather than directly se…
Deep Restricted Boltzmann Networks
Hengyuan Hu, Lisheng Gao, Quanbin Ma
Building a good generative model for image has long been an important topic in computer vision and machine learning. Restricted Boltzmann machine (RBM) is one of such models that i…
Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai +1
State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an effici…