Deep Dive: Is AI R&D Verifiable Enough for Recursive Self-Improvement?
Don't Worry About the Vase (Zvi) · rss · 2026-08-15
Zvi provides a detailed breakdown of a debate between Dwarkesh Patel and Ryan Greenblatt on Recursive Self-Improvement (RSI).
Feasibility of RSI:
Ryan argues AI R&D is highly verifiable (e.g., training loss), making it suitable for AI automation. Zvi counters that verifying alignment properties is terribly difficult. Focusing only on measurable capabilities invites Goodhart's Law, potentially creating a doom loop of RLVR on misaligned models.
Conceptual Leaps & Research Taste:
Dwarkesh questions AI's ability to make conceptual leaps in math and ML. Ryan suggests AI can handle 'baby's first new theory.' Zvi argues that frontier ML is about exploring possibilities, not optimizing fixed targets, and dismisses 'research taste' as a goalpost move that lacks training attempts.
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