A blog post discusses how reinforcement learning improvements for large language models disproportionately benefit easier problems while leaving harder ones largely unsolved—a phenomenon called the Matthew Effect. The authors propose a solution called Never Give Up to address this bias and improve performance on genuinely difficult tasks.