AI Models

LLM Enhancing LLM: How the GRPO Reward Update Framework Lowers the Barrier to Enterprise Proprietary Model Training

A study published in *Scientific Reports* in 2026 proposed a complete pipeline for using large language models (LLMs) to generate reasoning data and fine-tune another LLM, while also improving the GRPO (Group Relative Policy Optimization) objective function and introducing structured reward components. On the GSM8K and Buffett's shareholder letter datasets, the best model based on Qwen 2.5-3B-Instruct achieved an average token accuracy of over 98%, with training costs of only $78 to $82. This result is reshaping the cost structure for enterprises to acquire proprietary AI capabilities, making it lower than traditional manual annotation and large-scale full-parameter fine-tuning approaches.