5.6k citations · 16.3k across the 53 of their papers we have counts for
54 papers
NetHack is Hard to Hack
Ulyana Piterbarg, Lerrel Pinto, Rob Fergus
Neural policy learning methods have achieved remarkable results in various control problems, ranging from Atari games to simulated locomotion. However, these methods struggle in lo…
Accelerating exploration and representation learning with offline pre-training
Bogdan Mazoure, Jake Bruce, Doina Precup +2
Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement learning (RL) algorithms address this chall…
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Yilun Du, Conor Durkan, Robin Strudel +6
Since their introduction, diffusion models have quickly become the prevailing approach to generative modeling in many domains. They can be interpreted as learning the gradients of…
Collaborating with language models for embodied reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino +4
Reasoning in a complex and ambiguous environment is a key goal for Reinforcement Learning (RL) agents. While some sophisticated RL agents can successfully solve difficult tasks, th…
Distilling Internet-Scale Vision-Language Models into Embodied Agents
Theodore Sumers, Kenneth Marino, Arun Ahuja +2
Instruction-following agents must ground language into their observation and action spaces. Learning to ground language is challenging, typically requiring domain-specific engineer…
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer +6
Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR). In this paper, we aim to improve distillation methods that pave the…