GLM 5.2 With Vision

GLM 5.2, a leading open-source language model, has been successfully enhanced with vision capabilities without compromising its original text-only functions. This was achieved by training a small 2-layer MLP with 50 million parameters, which allowed GLM 5.2 to match Claude 4.5 Haiku's performance on the MMMU-Pro benchmark. The project involved adapting a vision tower from Kimi K2.6 and training only the vision projector to align visual inputs with GLM's language model. This alignment, facilitated by a process called grokking during supervised fine-tuning (SFT), allowed the model to generalize well, even recognizing famous individuals not present in the training data. The reinforcement learning phase restored GLM's ability to reason about images, despite initial struggles, and demonstrated the model's capability to generate reasoning traces after minimal updates. The successful integration of vision into GLM 5.2 opens up new possibilities for using the model in multimodal applications.

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