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Sina open sources VibeThinker-3B: reasoning can be compressed, factual knowledge cannot.

MeNews
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Abstract generation in progress
ME News message: On June 28 (UTC+8), Sina released VibeThinker-3B with only 3B parameters. It matches large models like DeepSeek V3.2, which are 200-333 times larger, on math and programming benchmarks including AIME26. On LiveCodeBench, it surpasses all models below 20B. In LeetCode contests, it solved 123/128 problems, outperforming GPT-5.2, Kimi K2.5, and others. However, it lags significantly behind on the knowledge-intensive GPQA-Diamond. The model is based on Alibaba’s Qwen2.5-Coder-3B, and was trained through multiple stages of post-training including SFT, reinforcement learning, and self-distillation. The research proposes the “parameter compression–coverage hypothesis”: logical reasoning depends on a small number of compressible patterns, while broad world knowledge still requires large parameters. The model has been open-sourced. 🔗 Read the original text:
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