RSI is Like NAS: Still Bounded by Search Space
kamalgupta09 · x · 2026-07-15
The author compares the research atmosphere around Recursive Self-Improvement (RSI) to NAS (Neural Architecture Search) around 2018: both are constrained by a "pre-defined search space."
He argues that NAS couldn't escape the convolutional network paradigm back then; similarly, while RSI can write new code, alter algorithms, and read papers, it remains limited by:
- External scaffolding
- Human-defined validation loops
The conclusion is that current RSI progress is real, but whether it can truly break through these artificial boundaries and achieve "escape velocity" remains unclear.
More from AGI Musings
- AI + science debate: the sweet spot is what happens to science, not scientists — soumitrashukla9 · 2026-09-11
- Economist Ben Moll: You Can Model Anthropic's 15% AI GDP Growth, But It Won't Happen — sebkrier · 2026-09-11
- Cohere Labs launches interactive tool mapping which tasks of 178 occupations AI can automate — Cohere_Labs · 2026-09-11
- AI researcher on SkyNews flags concerns over inequality, power and criminal misuse — schwarzjn_ · 2026-09-11
- VC compares AI doom rhetoric to pandemic-era fear messaging — StewartalsopIII · 2026-09-11
- Anthropic Insiders: Not Everyone at the Lab Believes in High p(doom) — anpaure · 2026-09-11