BEIJING, Aug. 27, 2026 /PRNewswire/ -- Recursive Self-Improvement is emerging as a new frontier in artificial intelligence. An increasing number of companies are incorporating coding agents into the development of AI models to improve efficiency—using AI to improve AI, or more surprisingly, to help build AI systems. This emerging trend is no longer merely a forward-looking concept circulating among a handful of leading AI labs. It has now been further validated by a series of results from ModelBest, a rising Chinese AI company.

At the end of May this year, ModelBest, in collaboration with the OpenBMB open-source community, released ForgeTrain, a production-level pre-training framework engineered by AI with no human in the loop. Using ForgeTrain, the team trained the base model of MiniCPM5-1B, a leading small language model of the world according to AA Index, at a training speed reportedly 10% faster than NVIDIA's Megatron. Later on, ModelBest continued to announce two further RSI technologies as follows:

During the World Artificial Intelligence Conference (WAIC) in July, ModelBest developed an agent powered by GLM-5.2 to iteratively optimize the training framework in only 18 hours, which then trained an AI model named MiniCPM5-130M from scratch. The resulting model turned out to match Gemma 3 270M in performance.

In August, ModelBest released ForgeStencil, a fully automated stencil optimization and deployment system engineered by AI, also with no human in the loop. This AI system optimized more than 100 stencils for industrial and scientific software in just one week.

Behind these achievements is the new technical paradigm, Forge Engineering, proposed by ModelBest. For decades, general-purpose frameworks have been the dominant paradigm of software engineering, which relies on expensive human programmers and comes at the cost of performance. But now, with the development of AI coding, it is possible to forge customized software for every demand at a lower and lower price.

The dramatic boost in training efficiency and sharp drop in compute costs prove the huge potential of this new paradigm in the most direct way. This landscape could have far-reaching implications for the future of AI. By developing AI systems faster and cheaper, more and more users can benefit from advanced technologies.