19 citations · 25 across the 11 of their papers we have counts for
11 papers
Self-Speculation for Faster Reasoning Models
Ravisri Valluri, Tung Nguyen, Aditya Grover
Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance on these tasks often requir…
HoneyBee: Data Recipes for Vision-Language Reasoners
Hritik Bansal, Devendra Singh Sachan, Kai-Wei Chang +4
Recent advances in vision-language models (VLMs) have made them highly effective at reasoning tasks. However, the principles underlying the construction of performant VL reasoning…
OpenThoughts: Data Recipes for Reasoning Models
Etash Guha, Ryan Marten, Sedrick Keh +47
Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoni…
When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
Nishad Singhi, Hritik Bansal, Arian Hosseini +4
Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-so…
VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
Hritik Bansal, Clark Peng, Yonatan Bitton +3
Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However,…
VideoPhy: Evaluating Physical Commonsense for Video Generation
Hritik Bansal, Zongyu Lin, Tianyi Xie +7
Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of…
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation
Hritik Bansal, Yonatan Bitton, Michal Yarom +3
Most of these text-to-video (T2V) generative models often produce single-scene video clips that depict an entity performing a particular action (e.g., 'a red panda climbing a tree'…
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Juan Nathaniel, Yongquan Qu, Tung Nguyen +4
Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond t…
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
Tung Nguyen, Rohan Shah, Hritik Bansal +6
Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep l…
VideoCon: Robust Video-Language Alignment via Contrast Captions
Hritik Bansal, Yonatan Bitton, Idan Szpektor +2
Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video c…
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
Experimental design is a fundamental problem in many science and engineering fields. In this problem, sample efficiency is crucial due to the time, money, and safety costs of real-…