31 citations · 34 across the 9 of their papers we have counts for
10 papers
Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models
Rishabh Tiwari, Aditya Tomar, Udbhav Bamba +5
Process Reward Models (PRMs) are rapidly becoming the backbone of LLM reasoning pipelines, yet we demonstrate that state-of-the-art PRMs are systematically exploitable under advers…
S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations
Arnav Chavan, Nahush Lele, Udbhav Bamba +3
Activation outliers in large-scale transformer models pose a fundamental challenge to model quantization, creating excessively large ranges that cause severe accuracy drops during…
CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Models
Neeraj Anand, Samyak Jha, Udbhav Bamba +1
Despite the rapid success of Large Vision-Language Models (LVLMs), a persistent challenge is their tendency to generate hallucinated content, undermining reliability in real-world…
XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation
Udbhav Bamba, Minghao Fang, Yifan Yu +2
Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of rollouts stabilizes training, existi…
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning
Udbhav Bamba, Neeraj Anand, Saksham Aggarwal +2
Binarization is a powerful compression technique for neural networks, significantly reducing FLOPs, but often results in a significant drop in model performance. To address this is…
UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images
Deepak K. Gupta, Udbhav Bamba, Abhishek Thakur +4
Convolutional neural network (CNN) approaches available in the current literature are designed to work primarily with low-resolution images. When applied on very large images, chal…