GLM 5.2 with vision

This post details the technical process of adding vision capabilities to the GLM 5.2 language model. It describes using a pre-trained vision tower from Kimi K2.6 and training only the vision projector to map its output to GLM's embedding space. The process involved two stages: Supervised Fine-Tuning (SFT) on image-question-answer pairs, observing 'grokking' in the loss curve around 900 steps, and Reinforcement Learning (RL) to restore reasoning capabilities. The authors highlight the surprising generalization of the model, identifying people not present in the training data. They also detail the training parameters (batch size 64, learning rate 5e-4) and the observation of rapid reward curve improvement during RL.