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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen2.5-Coder-7B |
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tags: |
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- code |
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--- |
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# Caco: Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning |
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[](https://arxiv.org/abs/2510.04081) |
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[](https://neurips.cc/) |
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[](https://opensource.org/licenses/Apache-2.0) |
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**Caco-CodeGen** is a code-driven reasoning generation model trained under the Caco framework. |
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It serves as the core engine for expanding executable Code Chain-of-Thoughts (Code CoTs), enabling diverse, verifiable, and pattern-aware reasoning data synthesis at scale. |
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--- |
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## 🚀 Overview |
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Traditional Chain-of-Thought (CoT) data often lacks **verifiability** and **diversity**. |
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**Caco** addresses this by grounding reasoning in *executable programs*, enabling automatic correctness checks and scalable reasoning synthesis. |
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| Property | Description | |
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| ---------------------- | -------------------------------------------------------------------------- | |
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| **Model Type** | Code LLM (Code-Aware Generator) | |
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| **Base Model** | Qwen2.5-Coder-7B | |
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| **Training Objective** | Next-token prediction on executable reasoning traces | |
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| **Training Data** | Code CoTs extracted and unified from math and algorithmic datasets | |
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| **Output Type** | Python-like executable reasoning steps (`code_cot`) | |
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| **Verification** | Code execution + output consistency filter | |
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--- |
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## 🧠 Methodology |
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<p align="center"> <img src="https://github.com/LHL3341/Caco/blob/main/caco.png?raw=true" alt="Caco Framework Overview" width="600"/> </p> |
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Caco constructs reasoning data through **three scalable stages**: |
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### 1. Unifying Code CoT |
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Collect diverse **seed reasoning traces** (mathematical + algorithmic), normalize them into a unified executable format. |
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### 2. Scaling Code CoT |
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Train a **Code Generator** to expand reasoning traces via **Pattern-level Augmentation** — restructuring logic (e.g., decomposition, reformulation, alternative solution paths). |
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### 3. Instruction Reversing |
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Back-translate executable reasoning into **natural language problems and solutions**, and apply **dual correctness verification**. |
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--- |
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## ⚙️ Usage |
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### Example Inference |
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```bash |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "LHL3341/Caco-CodeGen" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") |
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prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=1024) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### Example use cases |
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* Fine-tuning reasoning LLMs (math, logic, or code tasks) |
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* Verifiable reasoning data augmentation |
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* Program-based RL reward modeling (RLVR) |
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* Cross-domain reasoning transfer experiments |
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--- |
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## 📈 Benchmarks (Caco Models) |
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| Model | MATH | Olympiad | Theorem-QA | |
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| -------------------- | -------- | -------- | ---------- | |
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| DeepSeekMath-7B-Caco | 68.2 | 29.5 | 33.8 | |
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| Qwen2.5-7B-Caco | **82.4** | **46.5** | **46.0** | |
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| Llama3-8B-Caco | 70.6 | 34.1 | 31.0 | |
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Models trained on Caco show **consistent improvements** across multiple reasoning benchmarks and domains. |
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## 🔬 Citation |
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If you use **Caco** in your research, please cite: |
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```bibtex |
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@article{caco, |
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title={Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoning}, |
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author={Honglin Lin and Qizhi Pei and Xin Gao and Zhuoshi Pan and Yu Li and Juntao Li and Conghui He and Lijun Wu}, |
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journal={arXiv preprint arXiv:2510.04081}, |
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year={2025} |
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} |
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``` |
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--- |
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## 📜 License |
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Apache 2.0 — free for academic and commercial use, with attribution. |
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--- |
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## 🌱 Related Resources |
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* [🧠 Caco Paper (arXiv:2510.04081)](https://arxiv.org/abs/2510.04081) |
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* [🧩 Caco-1.3M Dataset](https://huggingface.co/datasets/LHL3341/Caco-1.3M) |
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--- |
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## 💡 Future Directions |
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* **Raising Difficulty:** integrate harder datasets (AM-Thinking-distill, DAPO) |
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* **Expanding Diversity:** add science, proofs, procedural planning |
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* **RL with Verifiable Rewards (RLVR):** use code execution as low-noise reward signal |
