66 citations · 134 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
LaViDa: A Large Diffusion Language Model for Multimodal Understanding
Shufan Li, Konstantinos Kallidromitis, Hritik Bansal +7
Modern Vision-Language Models (VLMs) can solve a wide range of tasks requiring visual reasoning. In real-world scenarios, desirable properties for VLMs include fast inference and c…
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents
Salman Rahman, Liwei Jiang, James Shiffer +7
Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work…
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,…