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