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Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoning
arXiv – CS AI|Xichen Zhang, Sitong Wu, Yinghao Zhu, Haoru Tan, Shaozuo Yu, Ziyi He, Jiaya Jia||3 views
🤖AI Summary
Researchers introduced Scaf-GRPO, a new training framework that overcomes the 'learning cliff' problem in LLM reasoning by providing strategic hints when models plateau. The method boosted Qwen2.5-Math-7B performance on the AIME24 benchmark by 44.3% relative to baseline GRPO methods.
Key Takeaways
- →Scaf-GRPO addresses the 'learning cliff' phenomenon where LLMs fail on difficult problems and receive zero-reward signals that stall learning.
- →The framework strategically injects tiered hints only when models reach learning plateaus, enabling progressive capability improvement.
- →Testing on Qwen2.5-Math-7B showed a 44.3% relative improvement in pass@1 scores on the challenging AIME24 mathematics benchmark.
- →The method uses Group Relative Policy Optimization with scaffolded guidance ranging from abstract concepts to concrete solution steps.
- →This approach represents a significant advance toward autonomous reasoning capabilities in large language models.
#llm#reinforcement-learning#reasoning#mathematics#policy-optimization#scaffolding#grpo#qwen#research#benchmarks
Read Original →via arXiv – CS AI
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