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Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO
arXiv – CS AI|Bowen Yu, Maolin Wang, Sheng Zhang, Binhao Wang, Yi Wen, Jingtong Gao, Bowen Liu, Zimo Zhao, Wanyu Wang, Xiangyu Zhao||1 views
🤖AI Summary
Researchers developed a three-stage curriculum learning framework that improves Chain-of-Thought reasoning distillation from large language models to smaller ones. The method enables Qwen2.5-3B-Base to achieve 11.29% accuracy improvement while reducing output length by 27.4% through progressive skill acquisition and Group Relative Policy Optimization.
Key Takeaways
- →New curriculum learning framework addresses the challenge of distilling verbose Chain-of-Thought reasoning into compact student models.
- →Three-stage approach includes masked shuffled reconstruction, GRPO-optimized masked completion, and targeted rewriting for failure cases.
- →Qwen2.5-3B-Base achieved 11.29% accuracy improvement on GSM8K dataset while reducing output length by 27.4%.
- →Method outperforms both instruction-tuned variants and existing distillation approaches.
- →Framework preserves CoT interpretability while enabling smaller models to learn efficient reasoning patterns.
#chain-of-thought#model-distillation#curriculum-learning#language-models#grpo#qwen#reasoning#efficiency#optimization
Read Original →via arXiv – CS AI
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