Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn't
Sina Weibo's VibeThinker-3B has just three billion parameters but matches models like DeepSeek V3.2 and Kimi K2.5 on math and coding benchmarks. Those models are up to 333 times larger. The secret isn't size but multi-stage post-training. The researchers propose a hypothesis based on their findings: logical reasoning compresses well into small models, but broad world knowledge does not. The article Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual
Overview
Sina Weibo's VibeThinker-3B has just three billion parameters but matches models like DeepSeek V3.2 and Kimi K2.5 on math and coding benchmarks. Those models are up to 333 times larger. The secret isn't size but multi-stage post-training. The researchers propose a hypothesis based on their findings: logical reasoning compresses well into small models, but broad world knowledge does not.
Details
The article Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn't appeared first on The Decoder.
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Originally published at the-decoder.com.
