# MIT 与 Sakana AI 提出 SIFT：低成本实现自我改进的编码智能体

- 来源：DAIR.AI (@dair_ai)
- 发布时间：2026-09-20 04:39
- AIHOT 分数：51
- AIHOT 链接：https://aihot.news/items/cmu8vej3n02odrorrpp47h3tq
- 原文链接：https://x.com/dair_ai/status/2101410759511322725

## AI 摘要

MIT 与 Sakana AI 的论文提出 SIFT（Self-Improvement via Fast Tree-search），验证自我改进编码智能体可行，并将运行成本降到 DGM 基线的约十分之一。

## 正文

Banger paper from MIT and Sakana AI.

They show that self-improving coding agents work.

The best part is that their approach, Self-Improvement via Fast Tree-search (SIFT), runs at a tenth of the CPU hours of DGM.

They reach 35.1 percent on Polyglot with o3-mini after 30 expansions. DGM reaches 30.7 percent after 80 nodes of tree search.

SIFT does it in under 50 CPU hours and under 5 hours of wall clock. The Qwen3-30B configuration runs its full search at 224 CPU hours and $34 of API spend, a tenth of the DGM baseline.

The saving comes from where the money goes.

Benchmark evaluation is the runtime bottleneck, so an LLM judge ranks candidate self-modifications first and only promising candidates get evaluated.

Judge quality decides the run.

On TerminalBench, gpt-5.4-high as the pairwise judge finds a 36.7 percent agent against a 29.2 percent starting point. gpt-5 finds 34.5 percent, and its top-ranked candidate is not the best agent its search produced.

Paper: https://academy.dair.ai/papers/self-improvement-via-fast-tree-search-2609.19526