AI RESEARCH
FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
arXiv CS.LG
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ArXi:2605.08520v1 Announce Type: new LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this cost comes from synchronized stage execution and imbalance inside each LLM-heavy stage. We present FlashEvolve, an efficient framework that replaces synchronized execution with asynchronous workers and queues, allowing different stages and steps to overlap. To handle data staleness.