<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Jacob Mapel — Research</title><description>Research notes by Jacob Mapel.</description><link>https://jacobmapel.com/</link><item><title>A JEPA world model that learns Mario by playing itself</title><link>https://jacobmapel.com/research/lemario-flywheel/</link><guid isPermaLink="true">https://jacobmapel.com/research/lemario-flywheel/</guid><description>We trained a self-supervised JEPA world model on NES Super Mario Bros and ran a closed-loop flywheel — collect, retrain the world model, train a policy in imagination, repeat — for fifteen iterations. The headline iter looked like a breakthrough; a five-seed re-run revealed it was a draw from a much wider distribution than we&apos;d been measuring. This is the project so far, and the redesign that comes next.</description><pubDate>Mon, 25 May 2026 00:00:00 GMT</pubDate></item><item><title>Sport-transfer SSL — what scaling up actually changed</title><link>https://jacobmapel.com/research/sport-transfer-ssl-followup/</link><guid isPermaLink="true">https://jacobmapel.com/research/sport-transfer-ssl-followup/</guid><description>After the first writeup, three follow-up questions kept nagging. With more compute we tested them all — and one downstream task we hadn&apos;t measured before flipped the story.</description><pubDate>Mon, 18 May 2026 00:00:00 GMT</pubDate></item><item><title>Sport-transfer self-supervised learning — what we learned</title><link>https://jacobmapel.com/research/sport-transfer-ssl/</link><guid isPermaLink="true">https://jacobmapel.com/research/sport-transfer-ssl/</guid><description>A one-week investigation into whether self-supervised pretraining on in-domain video improves transfer to new sports tasks — and what those experiments taught us about how to actually measure SSL progress.</description><pubDate>Fri, 15 May 2026 00:00:00 GMT</pubDate></item></channel></rss>