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

Researchers discovered that reinforcement learning (RL) improvements in large language models disproportionately benefit easier tasks while leaving harder tasks largely unsolved—a phenomenon they term the "Matthew Effect." To address this bias, they propose a "Never Give Up" approach to help models tackle harder problems more effectively, demonstrating this issue across math, code, and agentic reasoning benchmarks.