[2606.16140] VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models

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Computer Science > Artificial Intelligence

arXiv:2606.16140 (cs)

[Submitted on 15 Jun 2026]

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

Authors:Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang<br>View a PDF of the paper titled VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models, by Sen Xu and 8 other authors

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Abstract: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, and offline self-distillation. 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, and 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. 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.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2606.16140 [cs.AI]

(or<br>arXiv:2606.16140v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.16140

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Sen Xu [view email]<br>[v1]<br>Mon, 15 Jun 2026 02:57:19 UTC (552 KB)

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