Arnav Chavan, Chanhyuk Lee, Udbhav Bamba, Jaehoon Yoo, Seunghoon Hong, Aditi Raghunathan, Jinwoo Kim · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28559 · open-access PDF
连续流程语言模型提供了一条通往并行生成的路径,控制和加速工具无法分散传播,但在数十亿参数尺度上构建它们的有效技术仍然不太确立。我们介绍了Lafa(大流程语言模型),一个快速,高性能的流程语言模型,以数据高效的方式扩展到8B参数。Lafa是一个区块式流程模型,注重前缀和每个区块内的双向无同步流程。
Machine translation (Meta M2M100); the English original follows.
Continuous flow language models offer a path to parallel generation with tools for control and acceleration unavailable to discrete diffusion, yet effective techniques for building them at the billion-parameter scale remain less established. We present Lafa (Large flow language model), a fast, high-performing flow language model scaled to 8B parameters in a data-efficient manner. Lafa is a blockwise flow model with causal attention on the prefix and bidirectional, asynchronous flow within each block. This design enables efficient training by adapting pretrained autoregressive (AR) models and f
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.28559, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange