LLM Cache Bandit Revisited: Addressing Query Heterogeneity for Cost-Effective LLM Inference
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| Format: | Preprint |
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2025
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| _version_ | 1866916957411868672 |
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| author | Yang, Hantao Xie, Hong Lian, Defu Chen, Enhong |
| author_facet | Yang, Hantao Xie, Hong Lian, Defu Chen, Enhong |
| contents | This paper revisits the LLM cache bandit problem, with a special focus on addressing the query heterogeneity for cost-effective LLM inference. Previous works often assume uniform query sizes. Heterogeneous query sizes introduce a combinatorial structure for cache selection, making the cache replacement process more computationally and statistically challenging. We treat optimal cache selection as a knapsack problem and employ an accumulation-based strategy to effectively balance computational overhead and cache updates. In theoretical analysis, we prove that the regret of our algorithm achieves an $O(\sqrt{MNT})$ bound, improving the coefficient of $\sqrt{MN}$ compared to the $O(MN\sqrt{T})$ result in Berkeley, where $N$ is the total number of queries and $M$ is the cache size. Additionally, we also provide a problem-dependent bound, which was absent in previous works. The experiment rely on real-world data show that our algorithm reduces the total cost by approximately 12\%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM Cache Bandit Revisited: Addressing Query Heterogeneity for Cost-Effective LLM Inference Yang, Hantao Xie, Hong Lian, Defu Chen, Enhong Computation and Language This paper revisits the LLM cache bandit problem, with a special focus on addressing the query heterogeneity for cost-effective LLM inference. Previous works often assume uniform query sizes. Heterogeneous query sizes introduce a combinatorial structure for cache selection, making the cache replacement process more computationally and statistically challenging. We treat optimal cache selection as a knapsack problem and employ an accumulation-based strategy to effectively balance computational overhead and cache updates. In theoretical analysis, we prove that the regret of our algorithm achieves an $O(\sqrt{MNT})$ bound, improving the coefficient of $\sqrt{MN}$ compared to the $O(MN\sqrt{T})$ result in Berkeley, where $N$ is the total number of queries and $M$ is the cache size. Additionally, we also provide a problem-dependent bound, which was absent in previous works. The experiment rely on real-world data show that our algorithm reduces the total cost by approximately 12\%. |
| title | LLM Cache Bandit Revisited: Addressing Query Heterogeneity for Cost-Effective LLM Inference |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.15515 |