29 citations · 139 across the 13 of their papers we have counts for
6 papers · 1 filter
Dichotomy of Control: Separating What You Can Control from What You Cannot
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Future- or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), where the future outcome (i.e., return) associated with an observ…
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang +3
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations. This motivates much of the recent theoretical study…
Offline RL for Natural Language Generation with Implicit Language Q Learning
Charlie Snell, Ilya Kostrikov, Yi Su +2
Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by…
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning proble…
Multi-Game Decision Transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang +8
A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largel…
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems
Charlie Snell, Mengjiao Yang, Justin Fu +2
Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of pro…