Researchers propose BeaconKV, a KV cache compression method for Large Reasoning Models (LRMs) that uses beacon queries to guide compression, addressing memory bottlenecks from long Chain-of-Thought generation. The method aims to reduce GPU memory usage while maintaining model performance.
Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation, but the resulting key-value (KV) cache grows linearly with sequence length and creates severe memory bottlenecks, often exceeding GPU capacity for long reasoning traces. Existing KV cac
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