# 自我反思循环是否值得？新论文给出否定答案

- 来源：elvis (@omarsar0)
- 发布时间：2026-08-05 06:00
- AIHOT 分数：44
- AIHOT 链接：https://aihot.virxact.com/items/cmsf7gq811ihxro2e2aiik7yf
- 原文链接：https://x.com/omarsar0/status/2084761324786172347

## AI 摘要

一项新研究系统测试了自我反思循环的有效性：在1.5B、3B和7B开源模型上对比七种方法，基于两个数学基准各150道题，并计入所有生成token成本。全部36项对比中，没有任何方法稳定胜出；10项显著更差，且全部涉及模型检查自身输出，18项自我检查对比均为负面结果。Self-Refine和强制Reflexion在7B模型上比基线低3.6至10.1分。

## 正文

Finally a good paper testing whether self-reflection loops are worth it.

Setup： Seven methods， open models at 1.5B， 3B and 7B， two math benchmarks， 150 questions each.

Every generated token counted， including the ones spent on critiques， reflections， debate turns and checking. Each method compared against repeated sampling at its own measured cost， paired by question， with bootstrap intervals and multiplicity correction.

All 36 comparisons came back with no reliable win for any method. Ten are reliably worse， and every one of those is a method where the model inspects its own output. All 18 self-inspection comparisons are negative.

Self-Refine and a forced Reflexion sit 3.6 to 10.1 points below the baseline at 7B. Reflexion as published never triggered its own retry on the smallest model. It judged itself correct every time and quietly became a single chain of thought.

Worth knowing before you add another critique step to your agent loop.

Paper： https://arxiv.org/abs/2607.28576

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