Rohan Paul@rohanpaul_ai

2026-06-29 13:04·3天前

AI 摘要

剑桥大学、NVIDIA等机构发表新论文《The Red Queen Gödel Machine》，提出让AI智能体与评估者协同进化，避免固定基准导致的分数停滞或易被利用。每轮训练中，评估者冻结，同时用留出的人类/客观答案单独训练更强评估者，在安全交接点更新。在编程任务上，系统以1.35×-1.72×更少token超越此前最佳自改进编程智能体；论文写作中，协同进化的写作者获得审稿小组约1.86倍的平均接收率提升。论文强调更强AI需要更强的评估者与之共同成长。

New paper from Cambridge Univ+NVIDIA and other top labs teaches AI agents and AI judges to improve together， so neither side gets stuck.

Moves self-improving AI away from fixed benchmarks and toward a loop where the thing doing the judging can also get better.

The problem is that most self-improving agents train against a fixed benchmark or fixed evaluator， so the score can become stale， too easy， or easy to game.

The paper's idea is to let the evaluator improve too， but only at safe handoff points， so each training stretch still has a stable judge.

During each stretch， agents are tested by the current frozen evaluator， while possible better evaluators are tested separately against held-out human or objective answers.

The authors try this on coding， paper writing， paper reviewing， proof writing， and proof grading， where some tasks have clear answers and others need learned judgment.

On coding， the system beats the earlier best self-improving coding agent while using 1.35× to 1.72× fewer tokens， because a cheap code reviewer adds useful feedback.

On paper writing， the co-evolved writer gets about 1.86X higher average acceptance from a reviewer panel than the fixed-evaluator baseline.

The big point is that stronger AI systems may need stronger judges growing with them， because fixed tests can stop giving useful pressure.

----

Link - arxiv. org/abs/2606.26294

Title： "The Red Queen Gödel Machine： Co-Evolving Agents and Their Evaluators"

arXiv推理

Rohan Paul@rohanpaul_ai · X

56导出 Markdown