The proprietary seamless rollback engine developed by the MiMo project revolutionizes the RLHF training process. The engine integrates three core technologies: continuous rollback, asynchronous reward computation, and early termination, and by intelligently managing GPU computing resources, it increases the typical reinforcement learning training speed by 2.29 times and the verification speed by 1.96 times. On the technical principle, the system automatically adjusts the rollback nodes by monitoring the training status in real-time, significantly reducing the GPU idle waiting time.
In a training set of 130,000 math and programming problems, the technique enabled the training cycle of the RL version to be shortened from 7 days to 3 days with the traditional approach, while maintaining stable model quality. Although this feature is transparent to end users, the benefits it yields are directly reflected in the final performance of the model, especially the stability performance when dealing with complex mathematical derivations.
This answer comes from the articleMiMo: A Small Open Source Model for Efficient Mathematical Reasoning and Code GenerationThe
