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[ARTICLE · art-99270] src=news.ycombinator.com ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Idea for Expository AI

A Hacker News user proposed an RL environment to improve frontier AI models' math explanations, where a large model teaches a small 0.5-1B parameter model to solve hard problems, with rewards based on the small model's success and human oversight to prevent leaking solutions.

read1 min views2 publishedAug 17, 2026

| |||||||||||| 1 point by | I've heard some complaints about the frontier models still be bad at explaining math and was thinking of an RL environment that would help might be to:-Take very hard math problem with a verifiable answer -Have frontier model explain to a tiny model like (0.5-1B params and provably bad score on the problem) how to solve but not the solution, and reward the frontier model for prompts/explanations that helped the tiny model solve the problem Obviously some amount of human supervision is needed to weed out it giving too much information | ||||||||||| |

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