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.<br /> The article Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn't appeared first on The Decoder. [...]
Rating
Innovation
Pricing
Technology
Usability
We have discovered similar tools to what you are looking for. Check out our suggestions for similar AI tools.
Why Weibo’s tiny VibeThinker-3B has the AI world arguing over benchmarks again
On Sunday, a team of nine researchers at Sina Weibo — the Chinese social media giant better known for its microblogging platform than for cutting-edge artificial intelligence — quietly posted a 14 [...]
Weibo's new open source AI model VibeThinker-1.5B outperforms DeepSeek-R1 on $7,800 post-training budget
Another day in late 2025, another impressive result from a Chinese company in open source artificial intelligence.Chinese social networking company Weibo's AI division recently released its open [...]
Microsoft built Phi-4-reasoning-vision-15B to know when to think — and when thinking is a waste of time
Microsoft on Tuesday released Phi-4-reasoning-vision-15B, a compact open-weight multimodal AI model that the company says matches or exceeds the performance of systems many times its size — while co [...]
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]
New training method boosts AI multimodal reasoning with smaller, smarter datasets
Researchers at MiroMind AI and several Chinese universities have released OpenMMReasoner, a new training framework that improves the capabilities of language models in multimodal reasoning.The framewo [...]
Researchers say they trained a foundation model from scratch for about $1,500
Training a foundation LLM from scratch costs millions and requires internet-scale data — which is why most enterprises don't bother. Sapient thinks it has a cheaper path.To overcome this brute- [...]
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]