Why LLMs can crack Millennium Prize Problems but still ship broken UIs: verifiable rewards
iamsahaj_xyz · x · 2026-10-30
Responding to @ThePrimeagen's jab that OpenAI models can solve Millennium Prize Problems yet ship buggy UIs, the author explains the mechanism behind the gap: verifiable rewards.
- Math problems are binary — either solved or not — and LLMs can verify answers, even via formal proofs written as code (Lean).
- UI quality is subjective and hard to quantify; rules, skills, and guardrails still can't match the precision of unit tests.
This artifact of training explains why math and coding see massive breakthroughs while law, biology, and copywriting lag far behind. In theory, any field that can design a verifiable reward signal is one where models can rapidly improve.
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