字節跳動發佈實驗性擴散語言模型 Seed Diffusion Preview
字節跳動Seed團隊宣佈推出實驗性擴散語言模型Seed Diffusion Preview，標誌着在語言模型領域的一次重大技術突破。該模型旨在通過結構化的代碼生成實驗，驗證離散擴散技術路線作爲下一代語言模型基礎框架的可行性。Seed Diffusion Preview在推理速度上取得了顯著提升，達到每秒2146個tokens，相比同等規模的自迴歸模型提升了5.4倍，同時在多個代碼生成基準測試中表現出與自迴歸模型相當的性能。
Seed Diffusion Preview的發佈，旨在解決自迴歸（AR）模型在推理速度和全局控制方面的侷限性。擴散模型通過從粗到精的生成範式，在圖像和視頻合成等連續數據領域取得了顯著成功。然而，將擴散模型應用於自然語言等離散領域面臨根本性挑戰，主要在於標準擴散過程與離散狀態空間的不兼
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
