54 citations · 82 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
RanDeS: Randomized Delta Superposition for Multi-Model Compression
Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov +1
From a multi-model compression perspective, model merging enables memory-efficient serving of multiple models fine-tuned from the same base, but suffers from degraded performance d…
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…