7 papers
Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding
Marianne Arriola, Volodymyr Kuleshov
Discrete diffusion models have steadily improved in quality relative to autoregressive (AR) models. However, these models are normally constrained to fixed-length generation and do…
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Yair Schiff, Omer Belhasin, Roy Uziel +6
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance. Despite…
Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
Marianne Arriola, Yair Schiff, Hao Phung +2
Discrete diffusion models enable parallel token sampling for faster inference than autoregressive approaches. However, prior diffusion models use a decoder-only architecture, which…
d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation
Guanghan Wang, Gilad Turok, Yair Schiff +2
While diffusion language models (DLMs) have achieved competitive performance in text generation, improving their reasoning ability with reinforcement learning remains an active res…
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
Marianne Arriola, Aaron Gokaslan, Justin T. Chiu +5
Diffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeli…
Simple and Effective Masked Diffusion Language Models
Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff +5
While diffusion models excel at generating high-quality images, prior work reports a significant performance gap between diffusion and autoregressive (AR) methods in language model…