LLM inference produces one token at a time. An 8B flow language model challenges that assumption. Title: Large Flow Language Models URL: 🌊 This work adapts pretrained autoregressive models into Lafa, a blockwise flow language model. It trains on fewer than 30B tokens and reaches 2.54x the throughput of AR at 7B with comparable quality. ⚡ Highlight 1: Asynchronous flows Each token in a block gets its own time, with the denoising schedule learned from the loss. A left-to-right tendency emerges on its own, without being imposed. 🧠 Highlight 2: Self-speculative decoding A one-step flow drafts tokens, and the AR head kept inside the same model verifies them. The output matches greedy AR decoding while producing roughly 2.5 to 5 tokens per forward pass. 🪶 Highlight 3: Less training, better quality Built on Qwen2.5 and Qwen3, it uses at least 18x fewer training tokens than Dream, LLaDA, and Efficient-DLM. The 7B average accuracy is 60.9 versus 58.4 for AR SFT. A strong sign that continuous flows can be a practical foundation for parallel generation. #LanguageModels# #FastInference#