ToRA: A Tool-Integrated Reasoning Agent

[🌐 Website] • [📜 Paper] • [🤗 HF Models] • [🐱 GitHub]
[🐦 Twitter] • [💬 Reddit] • [🍀 Unofficial Blog]

Repo for "ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving" [ICLR'2024]

Figure 1: Comparing ToRA with baselines on LLaMA-2 base models from 7B to 70B.

🔥 News

• [2023/10/08] 🔥🔥🔥 All ToRA models released at 🤗 HuggingFace!
• [2023/09/29] ToRA paper, repo, and website released.

💡 Introduction

ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.

Model	Size	GSM8k	MATH	AVG@10 math tasks†
GPT-4	-	92.0	42.5	78.3
GPT-4 (PAL)	-	94.2	51.8	86.4
ToRA-7B	7B	68.8	40.1	62.4
ToRA-Code-7B	7B	72.6	44.6	66.5
ToRA-Code-7B + self-consistency (k=50)	7B	76.8	52.5	-
ToRA-13B	13B	72.7	43.0	65.9
ToRA-Code-13B	13B	75.8	48.1	71.3
ToRA-Code-13B + self-consistency (k=50)	13B	80.4	55.1	-
ToRA-Code-34B*	34B	80.7	51.0	74.8
ToRA-Code-34B + self-consistency (k=50)	34B	85.1	60.0	-
ToRA-70B	70B	84.3	49.7	76.9
ToRA-70B + self-consistency (k=50)	70B	88.3	56.9	-

• ^*ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!

• ^†10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.

Tool-Integrated Reasoning

Figure 2: A basic example of single-round tool interaction, which interleaves rationales with program-based tool use.

ToRA Training Pipeline

Figure 3: Training ToRA contains ① Imitation Learning, and ② output space shaping.

🚀 Quick Start

⚙️ Setup

We recommend using Conda to manage your environment. We use vLLM (0.1.4) to accelerate inference. Run the following commands to setup your environment:

git clone https://github.com/microsoft/ToRA.git && cd ToRA/src
conda create -n tora python=3.10
conda activate tora
pip install packaging==22.0
pip install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118 # CUDA 11.8 for example
pip install -r requirements.txt

🪁 Inference

We provide a script for inference, simply config the MODEL_NAME_OR_PATH and DATA in src/scripts/infer.sh and run the following command:

bash scritps/infer.sh

We also open-source the model outputs from our best models (ToRA-Code-34B and ToRA-70B) in the src/outputs/ folder.

⚖️ Evaluation

The src/eval/grader.py file contains the grading logic that assesses the accuracy of the predicted answer by comparing it to the ground truth. This logic is developed based on the Hendrycks' MATH grading system, which we have manually verified on the MATH dataset to minimize false positives and false negatives.

To evaluate the predicted answer, run the following command:

python -m eval.evaluate \
    --data_name "math" \
    --prompt_type "tora" \
    --file_path "outputs/llm-agents/tora-code-34b-v1.0/math/test_tora_-1_seed0_t0.0_s0_e5000.jsonl" \
    --execute

then you will get:

Num samples: 5000
Num scores: 5000
Timeout samples: 0
Empty samples: 2
Mean score: [51.0]
Type scores: {'Algebra': 67.3, 'Counting & Probability': 42.2, 'Geometry': 26.1, 'Intermediate Algebra': 40.0, 'Number Theory': 59.3, 'Prealgebra': 63.8, 'Precalculus': 34.2}

⚡️ Training

We're currently undergoing an internal review to open-source the ToRA-Corpus-16k, stay tuned! We also open-source our complete training scripts for the community, and you may construct your own dataset for training. We provide some example data in data/tora/.

To train a model, run the following command:

bash scripts/train.sh codellama 7b

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@inproceedings{
gou2024tora,
title={To{RA}: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Ep0TtjVoap}
}

🍀 Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

🌟 Star History