27 citations · 43 across the 28 of their papers we have counts for
18 papers · 1 filter
ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies
Tzu-Hsiang Lin, Srinivas Shakkottai, Dileep Kalathil +1
Behavior-cloned diffusion policies are expressive but remain vulnerable to covariate shift: small deviations from demonstrated states can compound into task failure. Existing metho…
Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh +3
Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging…
Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
Akshay Mete, Shahid Aamir Sheikh, Tzu-Hsiang Lin +2
Efficient exploration remains a central challenge in reinforcement learning (RL), particularly in sparse-reward environments. We introduce Optimistic World Models (OWMs), a princip…
GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels
Bhavya Sai Nukapotula, Rishabh Tripathi, Seth Pregler +3
Channel state information (CSI) is essential for adaptive beamforming and maintaining robust links in wireless communication systems. However, acquiring CSI incurs significant over…
MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection
Jeremy Carleton, Debajoy Mukherjee, Srinivas Shakkottai +1
Large Language Models (LLMs) are increasingly deployed across diverse applications that demand balancing multiple, often conflicting, objectives -- such as helpfulness, harmlessnes…
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
Shubham Parashar, Shurui Gui, Xiner Li +8
We aim to improve the reasoning capabilities of language models via reinforcement learning (RL). Recent RL post-trained models like DeepSeek-R1 have demonstrated reasoning abilitie…