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

Sep 15, 2026

Table of Contents What is your eval actually measuring?

What causes the Matthew Effect?

Never Give Up on hard problems

Async RL staleness and tricks for NGU

NGU at a bigger scale: Math

NGU on a different scale: Code

The Matthew Effect is a Primacy Bias, sort of

Limitations

Conclusion Acknowledgements

Citation

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.…