做智能体自循环的该看看这篇，剑桥让评估器和智能体一起进化，避免奖励黑客，思路很直接。
剑桥大学提出 Red Queen Gödel Machine，通过让智能体与评估器共同进化来解决自我改进停滞问题。传统自改进循环中智能体学会欺骗固定评估器，导致奖励黑客。新方法让评估器的难度随智能体能力提升而增加，保持循环持续有效。论文编号 arxiv.org/abs/2606.26294。
Fascinating paper on self-improving agents. (bookmark it) If you are working on agentic loops, you...
Fascinating paper on self-improving agents. (bookmark it) If you are working on agentic loops, you will quickly realize that they are only as good as the effectiveness of the evaluator. Self-improvement loops tend to stall the moment the judge stops getting harder. The agent learns to satisfy a fixed evaluator rather than getting genuinely better. The Red Queen Gödel Machine, from Cambridge, co-evolves the agent and its evaluator together, so the bar keeps rising as the agent climbs. The name borrows the evolutionary arms race. Both sides have to keep running to stay in place. A frozen evaluator is where reward hacking creeps into self-improvement. Co-evolving the judge is a structural answer to that, and it keeps the loop honest over many rounds. Paper: arxiv.org/abs/2606.26294 Learn to build effective AI agents in our academy: academy.dair.ai 💬 11 🔄 23 ❤️ 168 👀 12186 📊 71 ⚡