LongFlow: Efficient KV Cache Compression for Reasoning Models
Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this performance gain comes with substantially longer output sequences, leading to significantly increased deployment costs. In particular, long outputs require large KV caches, resulting in high memory consumption and severe bandwidth pressure during attention computation. Most existing KV cache optimization methods are designed for long-input, short-output scenarios and are ineffective for the long-output setting of reasoning models. Moreover, importance estimation in prior work is computationally expensive and becomes pr...
| Authors | Yi Su, Zhenxu Tian, Dan Qiao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 25 April 2026 |
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