Learning to solve hard problems in RL for LLMs by never giving up

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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. Her…

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