[Submitted on 30 Oct 2025 (
[v1](https://arxiv.org/abs/2510.26692v1)), last revised 1 Nov 2025 (this version, v2)]# Title:Kimi Linear: An Expressive, Efficient Attention Architecture
[View PDF](/pdf/2510.26692)
Abstract:We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated DeltaNet with a finer-grained gating mechanism, enabling more effective use of limited finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR) transition matrices, which substantially reduces computation compared to the general DPLR formulation while remaining more consistent with the classical delta rule.
We pretrain a Kimi Linear model with 3B activated parameters and 48B total parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA). Our experiments show that with an identical training recipe, Kimi Linear outperforms full MLA with a sizeable margin across all evaluated tasks, while reducing KV cache usage by up to 75% and achieving up to 6 times decoding throughput for a 1M context. These results demonstrate that Kimi Linear can be a drop-in replacement for full attention architectures with superior performance and efficiency, including tasks with longer input and output lengths.
To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.
Submission history #
From: Yulun Du [[view email](/show-email/19b5afec/2510.26692)]
**Thu, 30 Oct 2025 16:59:43 UTC (645 KB)**
[[v1]](/abs/2510.26692v1)**[v2]** Sat, 1 Nov 2025 12:05:18 UTC (691 KB)
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