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cs.AI2025
Planned Diffusion
Daniel Israel, Tian Jin, Ellie Cheng +4
Most large language models are autoregressive: they generate tokens one at a time. Discrete diffusion language models can generate multiple tokens in parallel, but sampling from th…
cs.CL2025
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
Tian Jin, Ellie Y. Cheng, Zack Ankner +6
Decoding with autoregressive large language models (LLMs) traditionally occurs sequentially, generating one token after another. An emerging line of work explored parallel decoding…
cs.LG2025
The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
Tian Jin, Ahmed Imtiaz Humayun, Utku Evci +4
Pruning eliminates unnecessary parameters in neural networks; it offers a promising solution to the growing computational demands of large language models (LLMs). While many focus…