这篇研究戳破了“大模型=好进化器”的直觉误区，做智能体开发的团队可以重新分配预算——用便宜模型写更新，贵模型做执行，效果反而更好。
一项新研究挑战了自我进化智能体的普遍假设，即更强的模型能写出更好的提示和技能编辑。实验表明，模型生成进化更新的能力在不同能力级别上基本持平，Qwen3.5-9B与Claude Opus 4.6表现相当。而受益于这些更新的能力呈倒U型曲线，中等模型效果最佳，弱模型无法激活更新，强模型改进空间有限。因此，建议将廉价模型用于进化器角色，昂贵模型用于求解器角色，以最大化收益。该发现对构建长期任务智能体的开发者具有重要指导意义。
Very good advice on self-improving agents. (bookmark it) This is something I am seeing in my own e...
Very good advice on self-improving agents. (bookmark it) This is something I am seeing in my own experiments with coding agents and harnesses for long-horizon tasks. What I have found is that stronger models do not always evolve better agents. The current believe in self-evolving agents is that a bigger model writes better prompt and skill edits, so devs put their best model in the evolver seat. New research shows that intuition is mostly wrong. The work separates two abilities that usually get conflated. Producing harness updates stays flat across model capability, so Qwen3.5-9B writes edits roughly as good as Claude Opus 4.6. Benefiting from those updates follows an inverted-U that peaks at mid-tier models, while weak models fail to even activate the edits and strong models have little headroom left. This is important to understand as it tells you where to spend. Put a cheap model on the evolver and your expensive model on the solver, because the gains land solver-side, not evolver-side. Paper: arxiv.org/abs/2605.30621 Learn to build effective AI agents in our academy: academy.dair.ai 💬 4 🔄 4 ❤️ 43 👀 3021 📊 20 ⚡