RLVR training dynamics reveal implicit curriculum in reasoning models

Researchers have developed a theory explaining how reinforcement learning with verifiable rewards (RLVR) aids large reasoning models in overcoming long-horizon challenges. Their analysis reveals that RLVR training naturally follows an implicit curriculum, where easier problems are mastered first and pave the way for more difficult ones. This learning progression is influenced by the smoothness of the problem difficulty spectrum, with smooth transitions leading to a stable 'relay regime' and abrupt discontinuities causing grokking-like phase transitions. The study also introduces new techniques adapted from Fourier analysis on finite groups to support its theoretical framework. AI

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IMPACT Provides a theoretical understanding of how RLVR training dynamics enable transformers to tackle complex reasoning tasks.

RANK_REASON Academic paper on a novel theoretical framework for reinforcement learning dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Reinforcement learning with verifiable rewards
• RLVR
• transformers
• Fourier analysis
• Zixin Wen

• paper
• other