A Survey of Weight Space Learning: Understanding, Representation, and Generation · HackerPapers discussionshttps://hackerpaper.kaipfister.de/p/2603.10090/Collected HN comments and submissions linking this paper.enWed, 04 Nov 2026 05:35:47 GMTA Survey of Weight Space Learning: Understanding, Representation, and Generation — mentioned by yurimohttps://news.ycombinator.com/item?id=50350096urn:hackerpapers:mention:2603.10090:50350096Tue, 03 Nov 2026 10:12:17 GMT<p>It depends on your interest, since even though it is a subfield it is diverse. Relevant to this work here is a general overview: https://arxiv.org/abs/2603.10090 Also check out Tracr: https://deepmind.google/research/publications/22295/ Here they attempt to compile static programs into weights. This is less of an overview but I remember reading this before, from the larger collection of posts: https://sidn.baulab.info/david_atkinson_project/</p><p><a href="https://hackerpaper.kaipfister.de/p/2603.10090/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LGA Survey of Weight Space Learning: Understanding, Representation, and Generation — mentioned by btuckerhttps://news.ycombinator.com/item?id=50342988urn:hackerpapers:mention:2603.10090:50342988Mon, 02 Nov 2026 20:12:15 GMT<p>This survey paper from earlier this year has a lot of good related work: A Survey of Weight Space Learning: Understanding, Representation, and Generation https://arxiv.org/pdf/2603.10090 https://github.com/Zehong-Wang/Awesome-Weight-Space-Learning</p><p><a href="https://hackerpaper.kaipfister.de/p/2603.10090/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.LG