The Matthew Effect in RL for LLMs: Why Early Success Can Sabotage Learning

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

In this blog post, Michael Noukhovitch introduces the Matthew Effect in reinforcement learning post-training for LLMs: early successes bias the model toward easy problems, causing it to neglect harder ones. He proposes 'Never Give Up' (NGU), a method that forces the model to keep trying on difficult problems. Experiments on Olmo 3.1 RL-Zero Math and code tasks show NGU improves performance, though limitations remain.