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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>OptiMind: Teaching LLMs to Think Like Optimization Experts · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.22979/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2509.22979/feed.xml" rel="self" type="application/rss+xml" /><item><title>OptiMind: Teaching LLMs to Think Like Optimization Experts — mentioned by trilogic</title><link>https://news.ycombinator.com/item?id=46757112</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.22979:46757112</guid><pubDate>Sun, 25 Jan 2026 19:08:46 GMT</pubDate><description>&lt;p&gt;New powerful (GPT based) model from Microsoft, Small, fast and functional all in one just because of the new paper. Developer: Microsoft Research, Machine Learning and Optimization (MLO) Group Model Architecture: Mixture-of-Experts (MoE) variant of the transformer architecture (gpt-oss family). Parameters: 20 Billion (3.6B activated) Inputs: Natural language optimization problem description. Context Length: 128,000 tokens Paper for the method used: https://arxiv.org/pdf/2509.22979&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.22979/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
