Researchers have introduced a new reinforcement learning post-training technique for large language models called Never Give Up to overcome a training phenomenon known as the Matthew Effect. The method's effectiveness is demonstrated through improved evaluation metrics during the training of the Olmo 3.1 RL-Zero Math model, with the team releasing an interactive blog post, technical paper, and open-source code to detail their approach.