VibeThinker: 3B param model that beats Opus 4.5 on reasoning with novel SFT+GRPO

Computer Science > Artificial Intelligence

[Submitted on 15 Jun 2026]

Title: VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models

This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime.

Building upon the Spectrum-to-Signal post-training paradigm, we systematically enhance the model through an optimized pipeline that includes:

• Curriculum-based supervised fine-tuning
• Multi-domain reinforcement learning
• Offline self-distillation

Experimental Results

Experimental evaluations demonstrate that VibeThinker-3B achieves frontier-level performance on highly demanding verifiable tasks. Specifically:

• It attains a score of 94.3 on AIME26 (improving to 97.1 with claim-level test-time scaling)
• An 80.2 Pass@1 on LiveCodeBench v6
• Exhibits strong out-of-distribution generalization with a 96.1% acceptance rate on recent unseen LeetCode contests

This effectively places it in the performance band of first-tier reasoning systems, matching or exceeding flagship models that are orders of magnitude larger, such as DeepSeek V3.2, GLM-5, and Gemini 3 Pro.

Furthermore, a score of 93.4 on IFEval confirms that this extreme reasoning enhancement does not compromise strict instruction controllability.

Theoretical Implications

Extending our previous 1.5B work, these findings motivate the Parametric Compression-Coverage Hypothesis, which views verifiable reasoning as compressible into compact reasoning cores, while open-domain knowledge and general-purpose competence require broad parameter coverage over facts, concepts, and long-tail scenarios.

This perspective suggests that compact models are not merely deployment-efficient substitutes, but a complementary path toward frontier-level performance in parameter-dense capability regimes.

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