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 .

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