小众架构赢麻了，通过编辑功能让100B扩散模型飙出892 tokens/秒的速度

Core Insights - The article highlights the significant advancements of Ant Group's LLaDA2.1 model, which has achieved a peak speed of 892 tokens per second in complex programming tasks, outperforming mainstream autoregressive models that operate at much lower speeds [1][18][20]. Group 1: Model Development and Features - LLaDA2.1 represents a historic shift from being a research model to a practical tool, showcasing improved efficiency and usability [2][5]. - The model introduces a dual-mode design, allowing users to switch between Speedy Mode and Quality Mode with a single configuration, thus simplifying user experience and model management [4][6]. - The Speedy Mode allows for rapid initial draft generation, while the Quality Mode focuses on accuracy, catering to different user needs [6][21]. Group 2: Technical Innovations - The model employs an Error-Correcting Editable (ECE) mechanism, enabling self-correction during the generation process, which addresses the common issues of inconsistency in earlier diffusion models [8][13]. - LLaDA2.1 successfully implements reinforcement learning (RL) on a 100B parameter diffusion model, a feat previously considered impossible, enhancing its performance in alignment tasks [16][22]. Group 3: Performance Metrics - In benchmark tests, LLaDA2.1 outperformed its predecessor LLaDA2.0 across various tasks, demonstrating superior performance in both speed and quality [22][23]. - The model's peak speed in Speedy Mode reached 892 tokens per second on the HumanEval+ benchmark, while the mini version exceeded 1500 tokens per second in certain tasks [18][24]. Group 4: Industry Implications - The advancements in LLaDA2.1 challenge the dominance of autoregressive models, suggesting a potential shift in industry standards towards more efficient and versatile models [20][26]. - The open-sourcing of LLaDA2.1 and its mini version indicates a strategic move to foster wider adoption and innovation within the AI community [24][27].