11 citations · 23 across the 9 of their papers we have counts for
11 papers
From Static Policies to Adaptive Priors in Offline Reinforcement Learning
Tianwei Ni, Vineet Jain, Akash Karthikeyan +1
Offline reinforcement learning (RL) has traditionally focused on learning policies for direct deployment under conservative objectives, where uncertainty outside the offline datase…
A Comedy of Estimators: On KL Regularization in RL Training of LLMs
Vedant Shah, Johan Obando-Ceron, Vineet Jain +10
The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL objective for LLM training involve…
Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism
Tianwei Ni, Esther Derman, Vineet Jain +3
Popular offline reinforcement learning (RL) methods rely on explicit conservatism, penalizing out-of-dataset actions or restricting rollout horizons. We question the universality o…
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal +9
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time co…
Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
Tejas Vaidhya, Ayush Kaushal, Vineet Jain +5
Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge. As the computational p…
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Vineet Jain, Kusha Sareen, Mohammad Pedramfar +1
Adapting a pretrained diffusion model to new objectives at inference time remains an open problem in generative modeling. Existing steering methods suffer from inaccurate value est…
Sampling from Energy-based Policies using Diffusion
Vineet Jain, Tara Akhound-Sadegh, Siamak Ravanbakhsh
Energy-based policies offer a flexible framework for modeling complex, multimodal behaviors in reinforcement learning (RL). In maximum entropy RL, the optimal policy is a Boltzmann…
Learning to Reach Goals via Diffusion
Vineet Jain, Siamak Ravanbakhsh
We present a novel perspective on goal-conditioned reinforcement learning by framing it within the context of denoising diffusion models. Analogous to the diffusion process, where…
On Diffusion Modeling for Anomaly Detection
Victor Livernoche, Vineet Jain, Yashar Hezaveh +1
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different var…
Mini-batch graphs for robust image classification
Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi
Current deep learning models for classification tasks in computer vision are trained using mini-batches. In the present article, we take advantage of the relationships between samp…
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
Avisek Lahiri, Vineet Jain, Arnab Mondal +1
Contemporary deep learning based medical image segmentation algorithms require hours of annotation labor by domain experts. These data hungry deep models perform sub-optimally in t…