79 citations · 250 across the 12 of their papers we have counts for
12 papers
Improving Large Language Model Fine-tuning for Solving Math Problems
Yixin Liu, Avi Singh, C. Daniel Freeman +2
Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-…
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman, Peter J. Liu, Lechao Xiao +13
Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smalle…
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Cole Gulino, Justin Fu, Wenjie Luo +19
Simulation is an essential tool to develop and benchmark autonomous vehicle planning software in a safe and cost-effective manner. However, realistic simulation requires accurate m…
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability
Juan Jose Garau-Luis, Yingjie Miao, John D. Co-Reyes +4
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world. Designing RL algorithms that optimize these objectives can…
Information is Power: Intrinsic Control via Information Capture
Nicholas Rhinehart, Jenny Wang, Glen Berseth +4
Humans and animals explore their environment and acquire useful skills even in the absence of clear goals, exhibiting intrinsic motivation. The study of intrinsic motivation in art…
Differentiable Architecture Search for Reinforcement Learning
Yingjie Miao, Xingyou Song, John D. Co-Reyes +4
In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL? Using the original DARTS as…
Evolving Reinforcement Learning Algorithms
John D. Co-Reyes, Yingjie Miao, Daiyi Peng +5
We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-…
Ecological Reinforcement Learning
John D. Co-Reyes, Suvansh Sanjeev, Glen Berseth +2
Much of the current work on reinforcement learning studies episodic settings, where the agent is reset between trials to an initial state distribution, often with well-shaped rewar…
Entity Abstraction in Visual Model-Based Reinforcement Learning
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang +5
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit i…
Guiding Policies with Language via Meta-Learning
John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev +5
Behavioral skills or policies for autonomous agents are conventionally learned from reward functions, via reinforcement learning, or from demonstrations, via imitation learning. Ho…
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John D. Co-Reyes, YuXuan Liu, Abhishek Gupta +3
In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into th…
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu, John D. Co-Reyes, Sergey Levine
Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. E…