3 citations · 3 across the 30 of their papers we have counts for
10 papers · 1 filter
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
Juntong Shi, Brian L. Trippe, Jure Leskovec +2
Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressi…
Mitigating Bias in Locally Constrained Decoding via Tractable Proposals
Meihua Dang, Linxin Song, Honghua Zhang +3
Generations from large language models often fail to conform to desired constraints such as JSON schema. Existing locally constrained decoding (LCD) approaches enforce constraints…
Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
Yangyi Shen, Tianjian Feng, Jiaqi Han +5
Diffusion Language Models (DLMs) offer order-agnostic generation that can explore many possible decoding trajectories. However, current decoding methods commit to a single trajecto…
Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
Jingyang Ou, Jiaqi Han, Minkai Xu +5
Reinforcement Learning (RL) has proven highly effective for autoregressive language models, but adapting these methods to diffusion large language models (dLLMs) presents fundament…
RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
Tianlang Chen, Minkai Xu, Jure Leskovec +1
Diffusion large language models (dLLMs) have shown great potential in large-scale language modeling, and there is an increasing interest in further improving the capacity to solve…
Mercury: Ultra-Fast Language Models Based on Diffusion
Inception Labs, Samar Khanna, Siddhant Kharbanda +10
We present Mercury, a new generation of commercial-scale large language models (LLMs) based on diffusion. These models are parameterized via the Transformer architecture and traine…