This is a decensored version of a model, made using Heretic v1.4.0

Abliteration parameters

Performance

Residual Geometry

g = mean of residual vectors for good prompts

g* = geometric median of residual vectors for good prompts

b = mean of residual vectors for bad prompts

b* = geometric median of residual vectors for bad prompts

r = refusal direction for means (i.e., b - g)

r* = refusal direction for geometric medians (i.e., b* - g*)

S(x,y) = cosine similarity of x and y

|x| = L2 norm of x

Silh = Mean silhouette coefficient of residuals for good/bad clusters

Residual Vectors Visualization

VibeThinker-3B

Introduction

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.

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).

💡 VibeThinker-3B achieves strong results across mathematics, coding, knowledge, and instruction-following benchmarks.

🔁 VibeThinker-3B achieves competitive results against first-tier reasoning models and reaches the performance range of top-tier systems on several verifiable reasoning benchmarks.

🏆 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.

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.

The training pipeline contains the following stages:

1. 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.

2. Multi-domain Reasoning RL

   • VibeThinker-3B reuses MaxEnt-Guided Policy Optimization (MGPO).
   • 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.

3. 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.

4. 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

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig


class VibeThinker:
    def __init__(self, model_path):
        self.model_path = model_path
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_path,
            low_cpu_mem_usage=True,
            torch_dtype="bfloat16",
            device_map="auto",
        )
        self.tokenizer = AutoTokenizer.from_pretrained(
            self.model_path,
            trust_remote_code=True,
        )

    def infer_text(self, prompt):
        messages = [{"role": "user", "content": prompt}]
        text = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
        )
        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)

        generation_config = dict(
            max_new_tokens=102400,
            do_sample=True,
            temperature=1.0,
            top_p=0.95,
            top_k=None,
        )
        generated_ids = self.model.generate(
            **model_inputs,
            generation_config=GenerationConfig(**generation_config),
        )
        generated_ids = [
            output_ids[len(input_ids):]
            for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
        ]

        return self.tokenizer.batch_decode(
            generated_ids,
            skip_special_tokens=True,
        )[0]


if __name__ == "__main__":
    model = VibeThinker("WeiboAI/VibeThinker-3B")
    prompt = "Your Prompt"
    print(model.infer_text(prompt))

License

The model repository is licensed under the MIT License.

Citations & References

If you use VibeThinker-3B in your research or product, please cite:

@misc{xu2026vibethinker3bexploringfrontierverifiable,
      title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models}, 
      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},
      year={2026},
      eprint={2606.16140},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2606.16140}, 
}