{"slug": "idea-for-expository-ai", "title": "Idea for Expository AI", "summary": "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.", "body_md": "| ||||||||||||\n1 point by |\nI'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 | |||||||||||\n|", "url": "https://wpnews.pro/news/idea-for-expository-ai", "canonical_source": "https://news.ycombinator.com/item?id=49326422", "published_at": "2026-08-17 03:57:19+00:00", "updated_at": "2026-08-17 04:10:30.511238+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research"], "entities": ["Hacker News"], "alternates": {"html": "https://wpnews.pro/news/idea-for-expository-ai", "markdown": "https://wpnews.pro/news/idea-for-expository-ai.md", "text": "https://wpnews.pro/news/idea-for-expository-ai.txt", "jsonld": "https://wpnews.pro/news/idea-for-expository-ai.jsonld"}}