307 citations · 868 across the 18 of their papers we have counts for
20 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…
Considering Length Diversity in Retrieval-Augmented Summarization
Juseon-Do, Jaesung Hwang, Jingun Kwon +2
This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. W…
DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models
Utkarsh Tiwari, Aryan Seth, Adi Mukherjee +3
We introduce DebateBench, a novel dataset consisting of an extensive collection of transcripts and metadata from some of the world's most prestigious competitive debates. The datas…
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification
Yashwanth M., Vaibhav Singh, Ayush Maheshwari +2
We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction…
VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models
Harshit, Tolga Tasdizen
The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts
Aditya Sharma, Michael Saxon, William Yang Wang
We present LoCoVQA, a dynamic benchmark generator for evaluating long-context extractive reasoning in vision language models (VLMs). LoCoVQA augments test examples for mathematical…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
GPT-4 Technical Report
OpenAI, Josh Achiam, Steven Adler +278
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-wor…
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin Reddy +4
Exploration in reinforcement learning is a challenging problem: in the worst case, the agent must search for high-reward states that could be hidden anywhere in the state space. Ca…
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H. Pong, Ashvin Nair, Laura Smith +2
Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and…
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair +3
Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies…
Outcome-Driven Reinforcement Learning via Variational Inference
Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister +2
While reinforcement learning algorithms provide automated acquisition of optimal policies, practical application of such methods requires a number of design decisions, such as manu…
Planning with Goal-Conditioned Policies
Soroush Nasiriany, Vitchyr H. Pong, Steven Lin +1
Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states an…
Contextual Imagined Goals for Self-Supervised Robotic Learning
Ashvin Nair, Shikhar Bahl, Alexander Khazatsky +3
While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of beha…
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
Brian Yang, Jesse Zhang, Vitchyr Pong +2
Standardized evaluation measures have aided in the progress of machine learning approaches in disciplines such as computer vision and machine translation. In this paper, we make th…
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin +3
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward f…
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal +1
Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challengi…
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong +2
Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning m…