# 递归自我改进路线图论文：完整 RSI 尚未到来

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-09-14 05:18
- AIHOT 分数：39
- AIHOT 链接：https://aihot.news/items/cmu0cb00k0g60roryhymk3ezb
- 原文链接：https://x.com/rohanpaul_ai/status/2099246355785056273

## AI 摘要

一篇递归自我改进（RSI）路线图论文指出，我们已看到 RSI 的零散雏形，但完整的递归自我改进尚未到来，多数所谓"自我改进 AI"只自动化了改进流程的部分环节。论文将进展划分为 5 个层级，从执行人类设计的改进到改变改进器、评估器或研究策略；低层级证据广泛，端到端 L5 证据仍局限于有界原型。

## 正文

Beautiful roadmap paper on Recursive self-improvement.

Concludes, we are already seeing pieces of RSI, but full recursive self-improvement is not here yet.

Most self-improving AI still cannot improve how it improves

Says that most things called "self-improving AI" today only automate parts of the improvement process.

Genuine recursive self-improvement would mean the AI can persistently improve not just its outputs, prompts, tools, or code, but eventually the mechanism that decides how future improvements are discovered, tested, and kept.

AI is already very strong at answering knowledge and reasoning questions, but still much weaker at doing long, multi-step tasks with tools, software, and changing environments.

The paper maps progress across 5 levels, from executing human-designed improvements to changing the improver, evaluator, or research policy used in later rounds.

That last step makes the process recursive: a successful update changes how future updates are discovered or judged.

The survey finds broad evidence for lower levels, while experience-driven learning and deployment adaptation are more domain-dependent and end-to-end L5 evidence remains concentrated in bounded prototypes.