Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn't

Sina Weibo has released VibeThinker-3B, a small language model that achieves performance comparable to significantly larger systems on mathematics and programming benchmarks. The developers utilized multi-stage post-training to demonstrate that reasoning capabilities can be effectively compressed into compact architectures. This finding suggests that while specialized problem-solving skills do not require massive parameter counts, factual knowledge retention remains tied to model scale. The research provides a new framework for balancing efficiency and logic in smaller AI deployments.