Taking GLM-5 as an Example, Exploring How Jiuzhang Intelligent Computing Cloud's Reinforcement Learning System Implements "Training-Inference Consistency"

The development of large models is undergoing a paradigm shift from pre-training stacking to post-training reinforcement learning. The reinforcement learning system built by Jiuzhang Intelligent Computing Cloud for GLM-5 focuses on solving the consistency problem between training and inference environments, ensuring the stability of model performance in real-world scenarios.

As the marginal returns of pre-training diminish, higher-order capabilities such as mathematical reasoning, code generation, and complex decision-making rely increasingly on activation during the post-training phase. Through a closed loop of generation, execution, and feedback, reinforcement learning enables models to possess continuous learning and self-correction capabilities, serving as a key path to improving agent performance.

Achieving "training-inference consistency" places higher demands on underlying computing infrastructure. This involves not only algorithm optimization but also tests the intelligent computing cloud's support capabilities in resource scheduling, environment simulation, and feedback mechanisms, becoming an important competitive dimension for continuous large model scaling.

This case indicates that large model competition has moved from one-time training to continuous training. Future improvements in model capabilities will span the entire process of generation, feedback, and iteration, and the deep integration of computing power and algorithms will become the core driving force for industry development.

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