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NewsTopic · 智能体自我改进2 min read·2026-10-13

a16z Podcast: MakerMaker.AI Founders Distinguish Recursive Self-Improvement from Automation

In an a16z podcast released on October 12, 2026, General Partner Martin Casado interviews MakerMaker.AI co-founders Dhaval Adjodah and Owen He. The episode description details their effort to separate true recursive self-improvement (RSI) from merely using AI to automate development tasks. They argue that closing the improvement loop does not guarantee runaway 'takeoff' and emphasize that measuring real progress requires accounting for both capability gains and resource costs.

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2026-10-12 22:30 GMT+8

In an a16z podcast published on October 12, 2026, General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to discuss the concept of "Recursive Self-Improvement" (RSI). According to the episode description, the founders aim to clarify what RSI actually means, distinguishing it from simply using AI to automate parts of AI development. They describe a goal of creating a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack.

The description notes that Adjodah and He explain why closing this loop still does not guarantee runaway "takeoff." They emphasize that measuring real progress requires accounting for both capability gains and the resources required to achieve them. Additionally, they share early signs from their own research indicating that self-improving systems can increase performance while improving efficiency, but they also dig into the practical challenges of proving those gains are real, citing issues like variance, reward hacking, and the difficulty of distinguishing genuine improvement from noise.

For readers interested in AI technical progress and research methodology, this episode offers a perspective from practitioners on the definitions and measurement standards of RSI. While the specific experimental data or full arguments cannot be verified through the description alone, the proposed framework of viewing capability as both numerator and denominator helps readers maintain skepticism regarding resource costs and measurement complexity when encountering claims of rapid AI advancement.

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  • 原始出处youtube.com↗
Topic

智能体自我改进

智能体自我改进正从理论走向工程落地,核心争议在于“闭环”是否必然导致失控。

5 steps4 piecesUpdated 2026-10-14 01:33

How it got here

  1. 2026-09-22Weco AI 发布 AIDE²,展示研究智能体通过递归自我改进在8天内发现7次代码优化,并在隐藏评估中保持性能提升。
  2. 2026-09-29SelfSearch 论文提交,提出免奖励信号搜索方法,智能体利用过往自我改进记录提升成功率,以4.03美元成本达到 Terminal-Bench 高分。
  3. 2026-09-30ComputerSD 论文提交,引入在线自蒸馏方法,将GUI执行反馈转化为逐步训练信号,提升电脑操作智能体在OSWorld上的表现。

What is still unsettled

  • 自我改进的收益是否会随着迭代次数增加而出现边际递减,还是像AIDE²所示那样持续泛化?
  • 如何建立有效的机制来检测智能体在自我修改过程中产生的隐性偏差或奖励黑客行为?
  • 当智能体能力超过人类评估周期时,现有的对齐测试标准是否仍然有效?

Also in this thread

  • OpenAI Researcher Noam Brown Explains Multi-Agent Scaling Limits and Alignment Risks2026-10-13
  • New self-distillation method modestly improves computer-use agent training2026-10-02
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