I’ve been diving deep into multimodal reasoning models for my research, and I’ve been frustrated with the gap between closed-source models like GPT-4.1 and open-source alternatives. Most open models either can’t handle complex visual reasoning or require massive compute resources. But then I stumbled upon Skywork-R1V3, a 38B parameter model that’s been making waves in the community.
What caught my attention was their claim of 76.0% accuracy on MMMU, which would put it on par with much larger proprietary models. So, I decided to put it through its paces and see how it performs.
The model’s technical approach is really interesting. Instead of training visual reasoning from scratch, the Skywork team found a way to transfer reasoning patterns from their existing text-based models into the multimodal domain. They used reinforcement learning during post-training, which seems to be key to its strong performance on complex reasoning tasks.
In my testing, the model consistently broke down problems into logical steps rather than just pattern matching. It handled mathematical problems with diagrams and scientific figure interpretation with ease. The fact that it’s fully open-source with quantized versions available makes it actually usable for research.
I’m curious if others have experimented with cross-modal transfer approaches like this, or if anyone else has found effective ways to get strong reasoning performance without massive scale. I’d love to hear thoughts on RL vs supervised approaches for this kind of multimodal reasoning.
The broader Skywork ecosystem is also worth exploring, with their reward models being downloaded over 750,000 times and helping multiple frontier models achieve strong benchmark results. There’s clearly some solid technical work happening there.