Addressing the Matthew Effect in Reinforcement Learning for Large Language Models with Never Give Up

The article introduces the Matthew Effect in reinforcement learning for large language models (LLMs), where training disproportionately improves easy tasks while neglecting hard problems. It proposes the Never Give Up (NGU) method to address this by dynamically adjusting sampling strategies to focus on challenging tasks.

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