Large Models "Teach" Large Models: How Reward-Updating GRPO Reduces AI Inference Training Costs and Improves Enterprise Model Customization Efficiency

Industry Context

In today's rapidly evolving AI industry, the biggest obstacle to deploying large models in enterprises is often not the models themselves, but the high-quality Chain-of-Thought data required for fine-tuning on specific domains. Obtaining such data is not only expensive but also requires expert annotation, severely constraining the adoption of AI in vertical industries such as finance, law, and healthcare. A recent study published in *Scientific Reports* proposed a solution that uses existing large models to automatically generate reasoning data and optimizes the target model by improving the GRPO (Group Relative Policy Optimization) reward function, reducing training costs to the level of a few hundred dollars. This advance is expected to reshape the economics of AI model customization.

Market Impact

The most direct industrial impact of this study lies in the sharp drop in training costs. Experiments show that a model based on Qwen 2.5-3B-Instruct achieved average token accuracies of 98.2% and 98.5% on GSM8K and the Buffett shareholder letters dataset, respectively, with training time of only 40-42 hours and costs of approximately $78-82. This figure is far below the traditional fine-tuning cost that often runs into thousands of dollars, meaning even small and medium-sized enterprises can afford model customization for their own business data. For computing power providers, low-cost training may generate more small-scale, high-frequency reasoning training demand, while model service providers will need to adjust their pricing strategies.

Competitive Landscape

In this technological trend, the following types of participants are likely to benefit: first, model toolchain companies, such as platforms offering data generation and training optimization services; second, the open-source model community, as low-cost training will attract more developers to conduct secondary development based on open-source models (e.g., Qwen); third, cloud service providers, because although the cost per training run decreases, the overall number of training runs may increase, driving up total computing power consumption. Traditional data annotation and manual fine-tuning service providers, however, face pressure, as the automated generation of reasoning data is replacing part of the manual workload. Notably, major AI labs such as OpenAI and Anthropic have stronger model generation capabilities, and the cost of calling their APIs as "teacher models" may become a new business model.

Enterprise Implications## Enterprise Implications | Enterprise Implications

For enterprise decision-makers, this research sends a clear signal: the threshold for model customization is dropping significantly. Enterprises should evaluate the scenarios in their business where reasoning data can be automatically generated and pay attention to open-source tools such as Huggify-Data and CoT Data Generator. At the same time, when introducing reinforcement learning objective functions such as GRPO, it is necessary to understand the design of their reward mechanisms, especially how structured reward components ensure output format compliance. Enterprises should avoid using general-purpose models directly for specific tasks, and instead achieve rapid adaptation through such low-cost training methods. In addition, since the research has made the dataset and model publicly available, enterprises can conduct secondary development on this basis to shorten the verification cycle.

Outlook | Outlook

Looking ahead over the next 12 months, low-cost model customization technologies will accelerate their penetration into vertical industries, especially finance and legal, because these two fields have obvious demand for document structuring and logical reasoning. Within 24 months, platforms offering "model fine-tuning as a service" for different industries may emerge, allowing enterprises to avoid managing the training process themselves. Within 3 years, as inference costs continue to decline, large model customization will move from niche experiments to becoming a default option for enterprises. At the same time, regulators need to pay attention to the data security and model consistency risks that this training approach may bring, such as how to avoid bias transmission when using teacher models to generate data.