The Path to AGI is Amortizing Search: Distilling Winning Trajectories into Model Intuition
shyamalanadkat · x · 2026-07-23
The author argues that while per-task search is often O(n), the crucial move towards AGI is amortization. This involves distilling every winning trajectory back into the generator model until the search process becomes pure intuition.
Using chemistry as an analogy, the author notes that early chemical synthesis relied on brute-force trial and error. Over centuries, successes were distilled into named reactions, selectivity rules, and retrosynthesis heuristics. Today, a chemist 'just knows' which route will work; that intuition is essentially compressed search, caching millions of failed experiments into a prior.
Similarly, for AI scientists/agents, the goal shouldn't be just making the lab search faster, but ensuring every experiment updates the underlying model that proposes the experiments.
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