324 citations · 2.8k across the 66 of their papers we have counts for
66 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
Why Language Models Hallucinate
Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala +1
Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
GPT-4o System Card
OpenAI, :, Aaron Hurst +416
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Yevgen Chebotar, Quan Vuong, Alex Irpan +22
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and auton…
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Jonathan N. Lee, Annie Xie, Aldo Pacchiano +4
Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trai…
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
David Brandfonbrener, Ofir Nachum, Joan Bruna
In recent years, domains such as natural language processing and image recognition have popularized the paradigm of using large datasets to pretrain representations that can be eff…
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum +4
The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model d…
Foundation Models for Decision Making: Problems, Methods, and Opportunities
Sherry Yang, Ofir Nachum, Yilun Du +3
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in re…
Learning Universal Policies via Text-Guided Video Generation
Yilun Du, Mengjiao Yang, Bo Dai +5
A goal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impress…
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
David Venuto, Sherry Yang, Pieter Abbeel +3
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learnin…
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal +48
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specif…
Contrastive Value Learning: Implicit Models for Simple Offline RL
Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum +1
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting wit…
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
Jonathan N. Lee, George Tucker, Ofir Nachum +2
In offline reinforcement learning (RL), a learner leverages prior logged data to learn a good policy without interacting with the environment. A major challenge in applying such me…
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…
Understanding HTML with Large Language Models
Izzeddin Gur, Ofir Nachum, Yingjie Miao +6
Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. Yet, their capabilities for HTML understanding -- i.e., parsing the raw HTML…
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang +3
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, eff…
Joint Representation Training in Sequential Tasks with Shared Structure
Aldo Pacchiano, Ofir Nachum, Nilseh Tripuraneni +1
Classical theory in reinforcement learning (RL) predominantly focuses on the single task setting, where an agent learns to solve a task through trial-and-error experience, given ac…
A Mixture-of-Expert Approach to RL-based Dialogue Management
Yinlam Chow, Aza Tulepbergenov, Ofir Nachum +3
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use r…
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of -functions can be leveraged as the primary so…
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…
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto, David Meger, Doina Precup +2
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value funct…