Sebastian Raschka 2026预测：Transformer统治依旧，但扩散模型正悄然崛起

Core Insights - The architecture competition for LLMs is entering a nuanced phase, with a shift from merely increasing model parameters to a focus on mixed architectures and efficiency tuning [1][4] - Transformer architecture is expected to maintain its status as the cornerstone of the AI ecosystem for at least the next few years, although adjustments in efficiency and mixed strategies are anticipated [4] - The rise of hybrid architectures and linear attention mechanisms is becoming a focal point in the industry, with models like DeepSeek V3 and R1 showcasing significant efficiency improvements [5][8] Group 1: Efficiency Wars - The industry is increasingly focusing on hybrid architectures and efficiency improvements, as demonstrated by models like DeepSeek V3, which significantly reduces KV Cache usage during inference [5] - The MoE architecture allows models to maintain a large parameter count (671 billion) while only activating 37 billion parameters during inference, highlighting a trend towards efficiency without sacrificing capacity [5] - Other models such as Qwen3-Next and Kimi Linear are adopting mixed strategies to balance long-distance dependencies and inference speed [8] Group 2: Diffusion Language Models - Diffusion language models (DLMs) are attractive due to their ability to generate tokens quickly and cost-effectively through parallel generation, contrasting with the serial generation of autoregressive models [10][11] - Despite their advantages, DLMs face challenges in integrating tool calls within response chains due to their simultaneous generation nature [11] - Research indicates that DLMs may outperform autoregressive models when high-quality data is scarce, as they can benefit from multiple training epochs without overfitting [17][19] Group 3: Super Data Learners - A recent paper suggests that DLMs could be superior learners in a data-scarce environment, achieving better performance than autoregressive models when trained on limited data [17][19] - The phenomenon known as "Crossover" indicates that while autoregressive models learn faster with ample data, DLMs excel when data is restricted [19] - Factors contributing to DLMs' advantages include their ability to model dependencies between any positions in the text, deeper training through iterative denoising, and inherent data augmentation through the noise process [21]