5 citations · 7 across the 10 of their papers we have counts for
11 papers
Solving Physics Olympiad via Reinforcement Learning on Physics Simulators
Mihir Prabhudesai, Aryan Satpathy, Yangmin Li +6
We have witnessed remarkable advances in LLM reasoning capabilities with the advent of DeepSeek-R1. However, much of this progress has been fueled by the abundance of internet ques…
Iterative Refinement Improves Compositional Image Generation
Shantanu Jaiswal, Mihir Prabhudesai, Nikash Bhardwaj +5
Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, a…
Self-Questioning Language Models
Lili Chen, Mihir Prabhudesai, Katerina Fragkiadaki +2
Can large language models improve without external data -- by generating their own questions and answers? We hypothesize that a pre-trained language model can improve its reasoning…
Can LLMs Lie? Investigation beyond Hallucination
Haoran Huan, Mihir Prabhudesai, Mengning Wu +2
Large language models (LLMs) have demonstrated impressive capabilities across a variety of tasks, but their increasing autonomy in real-world applications raises concerns about the…
Diffusion Beats Autoregressive in Data-Constrained Settings
Mihir Prabhudesai, Mengning Wu, Amir Zadeh +2
Autoregressive (AR) models have long dominated the landscape of large language models, driving progress across a wide range of tasks. Recently, diffusion-based language models have…
Maximizing Confidence Alone Improves Reasoning
Mihir Prabhudesai, Lili Chen, Alex Ippoliti +3
Reinforcement learning (RL) has enabled machine learning models to achieve significant advances in many fields. Most recently, RL has empowered frontier language models to solve ch…
Unified Multimodal Discrete Diffusion
Alexander Swerdlow, Mihir Prabhudesai, Siddharth Gandhi +2
Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches, which process tokens sequentially from lef…
On the Surprising Effectiveness of Attention Transfer for Vision Transformers
Alexander C. Li, Yuandong Tian, Beidi Chen +2
Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate t…
Video Diffusion Alignment via Reward Gradients
Mihir Prabhudesai, Russell Mendonca, Zheyang Qin +2
We have made significant progress towards building foundational video diffusion models. As these models are trained using large-scale unsupervised data, it has become crucial to ad…
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback
Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li +2
The advancements in generative modeling, particularly the advent of diffusion models, have sparked a fundamental question: how can these models be effectively used for discriminati…
Aligning Text-to-Image Diffusion Models with Reward Backpropagation
Mihir Prabhudesai, Anirudh Goyal, Deepak Pathak +1
Text-to-image diffusion models have recently emerged at the forefront of image generation, powered by very large-scale unsupervised or weakly supervised text-to-image training data…