Title: Learning to Evict from Key-Value Cache
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Model, Key-Value Cache Compression, Natural Language Processing

Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache. Existing eviction or compression methods reduce cost but rely on heuristics, such as recency or past attention scores, which serve only as indirect proxies for a token’s future utility and introduce computational overhead. We reframe KV cache eviction as a reinforcement learning (RL) problem: learning to rank tokens by their predicted usefulness for future decoding. To this end, we introduce KV Policy (KVP), a framework of lightweight per-head RL agents trained on pre-computed generation traces using only key and value vectors. Each agent learns a specialized eviction policy guided by a holistic reward, derived from future utility, that evaluates the quality of the ranking across all cache budgets, requiring no modifications to the underlying LLM or additional inference. Evaluated on the long-context benchmark RULER and the multi-turn dialogue benchmark OASST2-4k, KVP significantly outperforms baselines. Furthermore, zero-shot tests on standard downstream tasks indicate that KVP generalizes well beyond its training distribution. These results demonstrate that learning to predict future token utility is a powerful and scalable paradigm for adaptive KV cache management.

TL;DR: We reframe KV cache management from a heuristic-based problem to a learnable one, aiming to rank cache entries by their predicted future utility