arXiv:2602.10238v2 Announce Type: replace-cross 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 th
The increasing scale of Large Language Models is making efficient inference a critical bottleneck, driving the need for more sophisticated memory management techniques in KV caches.
This development represents a significant step towards more efficient and cost-effective deployment of LLMs, directly impacting the scalability and operational expenses of AI infrastructure.
The shift from heuristic-based KV cache eviction to a reinforcement learning approach fundamentally alters how LLM memory is managed, potentially leading to substantial improvements in inference speed and reduced memory footprint.
- · AI infrastructure providers
- · Cloud computing platforms
- · LLM developers
- · AI-powered application developers
- · Less memory-efficient LLM designs
- · Users with limited compute budgets relying on less optimized solutions
More cost-effective and scalable deployment of large language models becomes possible.
This efficiency gain could accelerate the adoption and development of even larger and more complex AI models.
Reduced operational costs for AI could lower barriers to entry for new AI applications, fostering greater innovation across various sectors.
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Read at arXiv cs.LG