KV-streams enables efficient scaling of agentic LLMs to longer horizons by streaming cached computations rather than recomputing them, making long-context RL training practical without sacrificing performance.
This paper introduces KV-streams, a technique that speeds up training of long-horizon agentic language models by streaming the key-value cache forward during context compaction instead of repeatedly refilling it. The method achieves 2.6-5x training speedup while maintaining performance, and shows that the streamed cache can retain information beyond the visible context window.