AINeutralarXiv – CS AI · 7h ago7/10
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Scaling Behaviors of LLM Reinforcement Learning Post-Training: An Empirical Study in Mathematical Reasoning
Researchers conducted a comprehensive empirical study on scaling laws for large language models during reinforcement learning post-training, using Qwen2.5 models ranging from 0.5B to 72B parameters. The study reveals that larger models demonstrate superior learning efficiency, performance can be predicted via power-law models, and data reuse proves highly effective in constrained environments, providing practical guidelines for optimizing LLM reasoning capabilities.