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README.md

@@ -1,3 +1,165 @@
 ---
 license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-3B
+tags:
+- math
+- code
+- reasoning
+- gpqa
+- instruction-following
+pipeline_tag: text-generation
+library_name: transformers
 ---
+
+# VibeThinker-3B
+
+<p align="center"><a href="https://github.com/WeiboAI/VibeThinker">GitHub</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://huggingface.co/WeiboAI/VibeThinker-3B">Hugging Face</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://arxiv.org/pdf/2606.16140">Technical Report</a></p>
+
+## 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.
+
+![alt text](pictures/Abstrct.png)
+
+## 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).
+
+![alt text](pictures/Acc_and_Scale.png)
+
+💡 VibeThinker-3B achieves strong results across mathematics, coding, knowledge, and instruction-following benchmarks. 
+
+![alt text](pictures/VibeThiinker-3B.png)
+
+🔁 VibeThinker-3B achieves competitive results against first-tier reasoning models and reaches the performance range of top-tier systems on several verifiable reasoning benchmarks.
+
+![alt text](pictures/VibeThinker-3B+CLR.png)
+
+🏆 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.
+
+![alt text](pictures/LeetCode.png)
+
+
+## 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.
+
+![alt text](pictures/Architecture.png)
+
+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**
+
+```python
+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:
+
+```bibtex
+@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}, 
+}
+```