Sebastian Raschka • 6/17/2026

VibeThinker-3B and the Strength of Post-Training

This article examines VibeThinker-3B, a 3.09B parameter model built on Qwen2.5-Coder-3B, which reportedly achieves near state-of-the-art results on coding and reasoning benchmarks. The focus is on the post-training pipeline, including synthetic data curation, multi-stage supervised finetuning, reinforcement learning with MGPO, and self-distillation. The author estimates training costs at $25k-$60k and notes the model's potential impact on small-model AI development. It is a tech-focused analysis relevant to machine learning and AI research.

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#Reinforcement Learning #Post Training #Small Language Models