PaperAgent

Oct 29, 2026 · Artificial Intelligence

Lafa: Block-Level Asynchronous Flow LLMs Achieve 2.5x Throughput Over Autoregressive Models

CMU, UC Berkeley, and Amazon introduce Lafa, a continuous flow language model that converts pretrained autoregressive models into block-level asynchronous denoisers, achieving 2.02–2.54x throughput over AR baselines at 8B scale with matched quality, using under 30B tokens for adaptation.