VibeThinker-3B: A 3B Dense Reasoning Model Built on Qwen2.5-Coder-3B With the Spectrum-to-Signal Post-Training Pipeline

A small AI model with 3 billion parameters, built on an existing coder model and trained with a technique called the Spectrum-to-Signal pipeline, achieves reasoning performance comparable to models hundreds of times larger on verifiable tasks like mathematics and coding. The model is designed as a specialist for problems where answers can be checked, rather than a general-knowledge system, and runs efficiently on standard hardware. It matters because it demonstrates that reasoning capability does not require massive scale, potentially making advanced reasoning accessible for cost-sensitive deployments and edge devices. The model uses supervised fine-tuning to explore multiple solution paths, reinforcement learning to strengthen correct ones, and can further improve answers at test time by having itself verify intermediate claims without adding parameters.