Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning

A recent study published on arXiv demonstrates that curriculum reinforcement learning, which involves training language models progressively from easy to hard tasks, significantly improves their reasoning capabilities. This curriculum approach allows models to build foundational skills before tackling more complex challenges, leading to enhanced performance. Specifically, the model DeepSeek-R1 showed notable effectiveness in addressing difficult mathematical and coding problems when trained using this method. The findings confirm that structuring learning tasks in increasing order of difficulty can bolster the reasoning abilities of large language models. This approach represents a promising direction for advancing AI systems' problem-solving skills without relying solely on raw computational power. Overall, the study underscores the value of curriculum reinforcement learning as a strategy to improve the sophistication and reliability of language model outputs.