16 citations · 21 across the 18 of their papers we have counts for
7 papers · 1 filter
Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models
Stipe Frković, Metod Jazbec, Christian A. Naesseth
Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidence-based samplers. However, r…
Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models
Shijian Xu, Andrea Miele, Metod Jazbec +3
Diffusion language models (dLLMs) promise fast inference by generating multiple tokens in parallel, but suffer severe performance degradation when parallel decoding is pushed too a…
Online Safety Monitoring for LLMs
Mona Schirmer, Metod Jazbec, Alexander Timans +3
Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed i…
Uncertainty Estimation for Molecular Diffusion Models
Paul Seij, Christian A. Naesseth, Stephan Mandt +1
Diffusion models have seen wide adoption for 3D molecular generation, yet they offer no principled signal of when a generated molecule is likely to be of low quality. We propose a…
Re-evaluating Confidence Remasking in Masked Diffusion Language Models
Stipe Frkovic, Metod Jazbec, Dan Zhang +3
Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel tok…
Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
Ha Manh Bui, Metod Jazbec, Eric Nalisnick +1
Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficient, O2O-RL is susceptible to…
A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models
Theo X. Olausson, Metod Jazbec, Xi Wang +4
Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on the tradeoff between speed and…