R-KV is a technique that compresses key-value cache entries by discarding repetitive tokens during LLM decoding, targeting memory reduction for reasoning models. It integrates with popular inference frameworks including vLLM, SGLang, and flash attention. The method focuses on math and reasoning benchmarks such as AIME24, supporting models like DeepSeek-R1-Distill-Llama-8B.