LLM Digest

Story

arxiv_cs_cl · Oct 26, 2026 · paper

Source brief

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

arxiv.orgOct 26, 2026
original source linked

In brief

Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response. Efficient generation depends not only on which tokens are easy to predic...

Feed lens

eval

Read the original at arxiv.org →Open in live feed Read that day’s brief

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

Earlier in this thread 4 items

Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs

Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models

EvoRouter: Cost-Efficient Multi-Agent Routing via Insight-Guided On-Policy Distillation

VaLoP: Value Gradient Lookahead and Pullback for Reward-Guided Diffusion