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Concept

Recursive Self-Improvement

Training loops in which a model (or another model) influences its own training during the forward pass, so capability gains compound without requiring proportionally more human-generated data or labels.

Tensions

If RSI lands, the implicit scaling law shifts from "more compute + more tokens" to "more compute + better feedback loops" — which favors labs with the largest coherent compute clusters and proprietary RL infrastructure, not whoever has the largest crawl. Whether RSI is the actual unlock or just a fundraising narrative is unsettled; Anthropic's pre-training hire of Karpathy is the most expensive bet on the bull case to date.

Related Concepts

scaling laws | Chinchilla scaling | continual learning

Last updated: May 25, 2026