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Learning to Solve Hard Problems in RL for LLMs by Never Giving Up

This is a blog post for my recent paper on RL post-training of LLMs: introducing the Matthew Effect and proposing to solve it with Never Give Up. It is presented interactively and less formally, more like how I give the talk. For a deeper, more technical dive, check out the paper on arxiv and code on github. What is your eval actually measuring? # Every good RL practitioner has no doubt seen an eval curve go up. Here is the AIME 2025 eval during our RL training of Olmo 3.1 RL-Zero Math(1) (1)see Olmo 3.1 blog post and arxiv

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PageSaver vs GoFullPage: Which Fits Your Screenshot and PDF Workflow?

GoFullPage makes it easy to capture an entire webpage. PageSaver is an alternative worth considering when you also want clean reading copies, Markdown exports, Firefox support, or tools for working with the PDF after you save it.We build PageSaver, PixelPDF, and PageSaver PDF for…

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