Kimi Linear: An Expressive, Efficient Attention Architecture

Kimi Linear is a novel hybrid linear attention architecture introduced to improve upon traditional full attention methods. It demonstrates superior performance across multiple scenarios, including both short and long context processing as well as reinforcement learning tasks. Central to its design is the Kimi Delta Attention module, which incorporates a refined gating mechanism to enhance efficiency and expressiveness. This module is credited with significantly advancing the architecture’s overall effectiveness. The development of Kimi Linear marks a notable progression in attention mechanisms within machine learning, as it balances computational efficiency with robust performance. Its capabilities have been documented in recent research shared on arXiv, highlighting its potential impact on future AI applications. This advancement aligns with ongoing efforts to optimize attention architectures for diverse and complex learning environments.