FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
Summary
FlashEvolve is a framework that accelerates LLM-based agent self-evolution by using asynchronous stage orchestration and artifact version tracking. It significantly improves throughput and token efficiency compared to synchronous execution methods.
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Paper page - FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
Source: https://huggingface.co/papers/2605.08520 Authors:
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Abstract
FlashEvolve enhances LLM-based evolution frameworks by implementing asynchronous execution and artifact version tracking to reduce computational bottlenecks while maintaining evolutionary quality.
LLM-based evolutionhas 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 replacessynchronized executionwithasynchronous workersand queues, allowing different stages and steps to overlap. To handle data staleness introduced by asynchrony, FlashEvolve tracks artifact versions and applies different policies to update, discard, or patch stale artifacts. Unlike weight-space staleness in asynchronous RL, language-space staleness is inspectable and repairable: a stale artifact is not just delayed work, but readable evidence that the LLM can reflect on, revise, and turn into useful evolution signal. FlashEvolve further improves throughput and token efficiency withspeculative stage completionandadaptive workflow control. OnGEPAworkloads, FlashEvolve improves proposal throughput by 3.5times on localvLLMand 4.9times onAPI servingover synchronousGEPA. The same design also applies to ACE and Meta-Harness.
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