Data compiled by Woofun AI shows that Sina Weibo has open-sourced VibeThinker-3B, a compact inference engine built upon the Qwen2.5-Coder-3B foundation. Through a Spectrum-to-Signal retraining methodology, the model amplifies correct solution signals via reinforcement learning while utilizing a 64K thinking space to preserve complex reasoning chains. This architectural shift enables the 3B-parameter system to rival flagship architectures like DeepSeek V3.2, GLM-5, and Gemini 3 Pro in mathematical and programming benchmarks.

The implementation of step-level self-assessment and self-distillation significantly enhanced performance, elevating the AIME26 math test score from 94.3 to 97.1. Developers introduced the 'Parameter Compression-Coverage Hypothesis,' positing that logical reasoning is highly compressible and rule-based, whereas open-domain knowledge retention demands massive parameter counts for memorization. Consequently, while VibeThinker-3B excels in structured reasoning, its coverage of common-sense knowledge remains inferior to larger models, highlighting a strategic focus on defining capability boundaries rather than replacing large-scale systems.