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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2508.15432/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2508.15432/feed.xml" rel="self" type="application/rss+xml" /><item><title>SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data — HN submission</title><link>https://news.ycombinator.com/item?id=45350786</link><guid isPermaLink="false">urn:hackerpapers:submission:2508.15432:45350786</guid><pubDate>Tue, 23 Sep 2025 18:13:04 GMT</pubDate><description>&lt;p&gt;Show HN: SyGra – Graph-oriented Synthetic data generation Pipeline for LLMs&lt;/p&gt;&lt;p&gt;We&amp;#x27;re open-sourcing SyGra, a framework for building reproducible synthetic-data pipelines for LLM training and evaluation (SFT, DPO, agent simulation, multimodal). Problem: High-quality datasets are scarce, expensive, and often sensitive. When teams turn to synthetic data, the difficulty isn&amp;#x27;t single prompts—it&amp;#x27;s the end-to-end system: designing branching/looping workflows, coordinating multiple inference backends/APIs and tool calls, enforcing validation + schema compliance + quality tagging at scale, and running fault-tolerant jobs with resumability, sharding, and streaming. Ad-hoc…&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2508.15432/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
