RSI Paper: No Fast Takeoff in Sight
GaryMarcus · x · 2026-07-15
This repost discusses a paper on AI Recursive Self-Improvement (RSI). The core conclusion is that experimental results show clear diminishing returns with each round of self-improvement. The fitted growth relationship is roughly [Intelligence]^0.075, falling far short of the linear or superlinear feedback needed for "runaway self-improvement."
The author adds two key takeaways:
- While this research is commendable, there are currently no signs of a "fast takeoff" or singularity.
- The self-improvement observed in the paper occurs on specific training sets and metrics; when measured on training-set-agnostic capability scales, actual gains might be even weaker, approaching log-linear growth.
Related event: AIDE² Self-Improvement Run Beats 2 Years of Manual Tuning(12 posts)→
More from AGI Musings
- The Evolution of LLM Business Models: Selling Outcomes Over Tokens — yacineMTB · 2026-07-22
- Bindu Reddy says GPT-6 is coming soon, with Alibaba, DeepSeek and Kimi close behind — bindureddy · 2026-07-22
- Bindu Reddy says the industry still lacks a way to train 20T models and scale post-training RL — bindureddy · 2026-07-22
- Advanced AI Models Are Becoming Impossible to Plug and Play — emollick · 2026-07-22
- AI suggested a better composition, and that made one user uneasy — Sydde · 2026-07-22
- The Thimble and the Waterfall: AI's Data Bottleneck and Feedback Loops — dyamins · 2026-07-22