# Explore–Execute Chain (E2C) Model

This repository hosts the **pretrained and fine-tuned Explore–Execute Chain (E2C) models**.

**Paper:** *Explore–Execute Chain: Towards an Efficient Structured Reasoning Paradigm*  
*Kaisen Yang, Lixuan He, Rushi Shah, Kaicheng Yang, Qinwei Ma, Dianbo Liu, Alex Lamb*  
> Under review at ICLR 2026  

**Code:** [GitHub – Explore–Execute Chain](https://github.com/yks23/Explore-Execute-Chain)

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## 📖 Model Overview

E2C is a **two-stage reasoning framework** designed to improve the efficiency and interpretability of large language models (LLMs):

1. **Exploration** — Generate lightweight reasoning sketches (plans).  
2. **Execution** — Execute selected plans faithfully for high-quality results.

**Benefits:**
- Efficient reasoning with minimal computation  
- Explicit, interpretable exploration traces  
- Easy domain adaptation with minimal supervision  

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## 🚀 Key Features

- **Two-stage training**  
  - **E2C-SFT** — Supervised fine-tuning on exploration–execution pairs  
  - **E2C-RL** — Reinforcement learning to refine execution  
- **Efficient adaptation (EF-SFT)** — Adapt with exploration-only data  
- **Test-time scaling** — Aggregate multiple explorations for better results  
- Benchmarked on **mathematical** and **medical reasoning** datasets  

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## 📥 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "KaisenYang/Explore-Execute-Chain"
model_type = "8B-Final"  # change to the subfolder you want to use

tokenizer = AutoTokenizer.from_pretrained(model_name, subfolder=model_type)
model = AutoModelForCausalLM.from_pretrained(model_name, subfolder=model_type)

# Test example: Fibonacci sequence
inputs = tokenizer("What is the 10th number in the Fibonacci sequence?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
````

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## 🔗 Links

* 📂 **Full code and experiments:** [GitHub Repository](https://github.com/yks23/Explore-Execute-Chain)
* 📜 **Paper (under review):** ICLR 2026 submission

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## 📜 Citation

If you use this work, please cite:

```bibtex
@inproceedings{yang2026explore,
  title={Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm},
  author={Yang, Kaisen and He, Lixuan and Shah, Rushi and Yang, Kaicheng and Ma, Qinwei and Liu, Dianbo and Lamb, Alex},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026},
  note={under review}
}
```

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## 🧾 License

This project is licensed under the **MIT License**.
See the [LICENSE](https://github.com/yks23/Explore-Execute-Chain/blob/main/LICENSE) file for details.