301 citations · 338 across the 8 of their papers we have counts for
8 papers
Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries
Blair Yang, Fuyang Cui, Keiran Paster +4
The rapid development and dynamic nature of large language models (LLMs) make it difficult for conventional quantitative benchmarks to accurately assess their capabilities. We prop…
OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text
Keiran Paster, Marco Dos Santos, Zhangir Azerbayev +1
There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of…
Llemma: An Open Language Model For Mathematics
Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster +6
We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics,…
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
Shalev Lifshitz, Keiran Paster, Harris Chan +2
Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces a methodology, inspired by unCLIP, fo…
Large Language Models Are Human-Level Prompt Engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han +4
By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depend…
You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments
Keiran Paster, Sheila McIlraith, Jimmy Ba
Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their s…
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
Planning from Pixels using Inverse Dynamics Models
Keiran Paster, Sheila A. McIlraith, Jimmy Ba
Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by l…