VibeThinker-3B is a further exploration of the VibeThinker series at the 3B-parameter scale, focusing on challenging reasoning tasks with clear verification signals, such as mathematics, coding, and STEM. By systematically optimizing the Spectrum-to-Signal Principle (SSP) post-training pipeline introduced in VibeThinker-1.5B, VibeThinker-3B achieves strong performance on AIME, HMMT, IMO-AnswerBench, LiveCodeBench, and recent LeetCode contests, reaching the performance range of top-tier frontier reasoning models, including Qwen3.6 Plus, Gemini 3 Pro, GLM-5, and Kimi K2.5, on verifiable reasoning benchmarks.
Motivated by these observations, we propose the Parametric Compression-Coverage Hypothesis: different capabilities depend on model parameters in fundamentally different ways. Verifiable reasoning is closer to a highly compressible, parameter-dense capability, centered on multi-step reasoning, constraint satisfaction, self-correction, and answer verification. When the task space is sufficiently structured and feedback signals are sufficiently reliable, compact models may also carry near-frontier reasoning capabilities. In contrast, open-domain knowledge, general-purpose dialogue, and long-tail scenario understanding rely more heavily on large-scale parameters to broadly cover facts, concepts, and world knowledge.
From VibeThinker-1.5B to VibeThinker-3B, our goal is not to build a small model that replaces large-scale models, but to examine the real boundaries of small models along specific capability dimensions. With VibeThinker-3B, we aim to show that small models should not be viewed merely as a compromise for reducing deployment costs. For capability domains with clear feedback and verification mechanisms, SLMs emerge as a promising research trajectory toward frontier-level performance that is fundamentally complementary to the traditional parameter scaling paradigm.
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Key Performance Data
📏 In terms of reasoning accuracy relative to model scale, VibeThinker-3B reaches 76.4 on IMO-AnswerBench, a highly challenging benchmark with 400 IMO-level problems, with only 3B parameters, and improves to 80.6 with Claim-Level Reliability Assessment (CLR), a test-time scaling strategy for answer-verifiable reasoning tasks. This demonstrates that a model within a strictly small-model regime can reach the performance range of substantially larger models, such as DeepSeek V3.2 (78.3, 671B), GLM-5 (82.5, 744B), and Kimi K2.5 (81.8, 1T).
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💡 VibeThinker-3B achieves strong results across mathematics, coding, knowledge, and instruction-following benchmarks.
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🔁 VibeThinker-3B achieves competitive results against first-tier reasoning models and reaches the performance range of top-tier systems on several verifiable reasoning benchmarks.
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🏆 To further test the model's out-of-distribution performance, we evaluate VibeThinker-3B on recent unseen LeetCode weekly and biweekly contests (Python) from Apr. 25 to May 31, 2026. VibeThinker-3B passes 123/128 first-attempt submissions, corresponding to a 96.1% acceptance rate.
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Training Pipeline
VibeThinker-3B follows the Spectrum-to-Signal Principle (SSP) introduced in VibeThinker-1.5B. The SFT stage constructs a broad spectrum of valid reasoning trajectories, while the RL stage amplifies correct reasoning signals using verifiable rewards.
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The training pipeline contains the following stages:
Curriculum-based two-stage SFT
Stage 1 focuses on broad capability coverage across math, code, STEM reasoning, general dialogue, and instruction following.
Stage 2 shifts toward harder and longer-horizon reasoning samples.
Diversity-Exploring Distillation is used to preserve multiple valid solution paths.
RL is applied sequentially to math, code, and STEM reasoning tasks.
Training uses a single 64K long-context window to preserve complete long-horizon reasoning trajectories.
Offline Self-Distillation
High-quality trajectories from Math, Code, and STEM RL checkpoints are filtered and distilled back into a unified student model.
A learning-potential score is used to prioritize traces that are correct but not yet well modeled by the student.
Instruct RL
The final stage improves controllability on user-facing prompts.
Rule-based validators and rubric-based reward models are used for format-sensitive and open-ended instruction data.
Usage Guidelines
We recommend using VibeThinker-3B for competitive-style math, coding, STEM reasoning, and other tasks where the target answer can be verified. For broad open-domain knowledge tasks, larger general-purpose models may still be more suitable.
For benchmark-style evaluation, the technical report uses vLLM with:
temperature=1.0
top_p=0.95
top_k=-1
Quick Start
Required: transformers>=4.54.0
Recommended for better inference performance: vLLM==0.10.1 or SGLang>=0.4.9.post6
The model repository is licensed under the MIT License.
Citations & References
If you use VibeThinker-3B in your research or product, please cite:
bibtex
1@misc{xu2026vibethinker3bexploringfrontierverifiable,
2 title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models},
3 author={Sen Xu and Shixi Liu and Wei Wang and Jixin Min and Yingwei Dai and Zhibin Yin and Yirong Chen and Xin Zhou and Junlin Zhang},
4 year={2026},
5 eprint={2606.16140},
6 archivePrefix={arXiv},
7 primaryClass={cs.AI},
8 url={https://arxiv.org/abs/2606.16140},
9}
