刚刚，Kimi开源新架构，开始押注线性注意力

Core Insights - The article discusses the advancements in attention mechanisms, particularly focusing on the Kimi Linear architecture, which combines linear attention and full attention to improve efficiency and performance in various tasks [1][2][4]. Group 1: Kimi Linear Architecture - Kimi Linear introduces a new hybrid linear attention architecture called Kimi Delta Attention (KDA), which optimizes memory usage in limited state RNNs through a more efficient gating mechanism [4][10]. - The architecture features a 3:1 ratio of KDA layers to periodic full attention layers, significantly reducing memory usage while maintaining or exceeding the quality of full attention [10][32]. - Kimi Linear has a total of 48 billion parameters, with 3 billion activated parameters, and can handle context lengths of up to 1 million tokens [5][10]. Group 2: Performance and Efficiency - Kimi Linear demonstrates superior performance across various tasks, outperforming traditional full attention methods, especially in long-context tasks, by reducing the need for large key-value caches by up to 75% [5][10]. - The model achieves a decoding throughput that is six times faster than complete multi-head attention models when processing long contexts [5][59]. - In comparative evaluations, Kimi Linear consistently outperforms baseline models like MLA and GDN-H in general knowledge, reasoning, and Chinese tasks [44][49]. Group 3: Technical Innovations - The KDA mechanism introduces fine-grained control over memory decay and position awareness, enhancing the model's expressiveness and efficiency [20][24]. - The architecture employs a block-wise recursive and intra-block parallel strategy to maximize matrix multiplication throughput, leveraging Tensor Cores effectively [26][59]. - The NoPE (No Position Encoding) design in Kimi Linear allows for efficient long-context training by delegating position information responsibilities to KDA layers [34][39]. Group 4: Experimental Results - Kimi Linear achieved the highest average scores in long-context benchmarks, demonstrating its effectiveness in handling extensive sequences [52][53]. - In reinforcement learning scenarios, Kimi Linear showed faster and better performance improvements compared to MLA, particularly in mathematical reasoning tasks [56][57]. - The model's efficiency remains high, with negligible latency overhead compared to GDN-H during pre-filling, while showing significant speed advantages as sequence lengths increase [59][60].