Uploaded September 2025 | Updated September 2026, 2 hours ago
Meta just dropped something wild: MobileLLM-R1, a family of sub-1B reasoning models trained on just 4.2T tokens (10x less than Qwen) yet beating other open-source models by 2x–5x. These aren’t chatbots but pure text-to-text reasoning machines, tuned for math, Python/C++, and scientific logic.
The magic? Smart data efficiency: 4T high-quality tokens (FineWeb-Edu, StarCoder), mid-training distilled from Llama-3.1-8B, and 6.2M post-training reasoning samples. It shows you don’t need 36T tokens to hit state-of-the-art under 1B parameters. That said, keep in mind the “10x less data” claim quietly includes Llama distillation.
You get open-weights, full recipes, a 32k context window, and a non-commercial license. Huge for reproducibility, even if not for production chatbots.
Let me know which AI news I should break down next, and I’ll tag you.
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#MetaAI #MobileLLM #AInews #short
Meta just dropped something wild: MobileLLM-R1, a family of sub-1B reasoning models trained on just 4.2T tokens (10x less than Qwen) yet beating other open-source models by 2x–5x. These aren’t chatbots but pure text-to-text reasoning machines, tuned for math, Python/C++, and scientific logic.
The magic? Smart data efficiency: 4T high-quality tokens (FineWeb-Edu, StarCoder), mid-training distilled from Llama-3.1-8B, and 6.2M post-training reasoning samples. It shows you don’t need 36T tokens to hit state-of-the-art under 1B parameters. That said, keep in mind the “10x less data” claim quietly includes Llama distillation.
You get open-weights, full recipes, a 32k context window, and a non-commercial license. Huge for reproducibility, even if not for production chatbots.
Let me know which AI news I should break down next, and I’ll tag you.
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀
#MetaAI #MobileLLM #AInews #short
