Step-DPO
Implementation for "Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs"
Stars: 155
Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.
README:
Xin Lai, Zhuotao Tian, Yukang Chen, Senqiao Yang, Xiangru Peng, Jiaya Jia
This repo provides the implementation of Step-DPO, a simple, effective, and data-efficient method for boosting the long-chain reasoning ability of LLMs, with a data construction pipeline that yields a high-quality dataset containing 10K step-wise preference pairs.
Notably, Step-DPO boosts the performance of Qwen2-7B-Instruct from 53.0% to 58.6% on MATH, and 85.5% to 87.9% on GSM8K, with as few as 10K data and hundreds of training steps!
Moreover, Step-DPO, when applied to Qwen2-72B-Instruct, achieves scores of 70.8% and 94.0% on the test sets of MATH and GSM8K, respectively, surpassing a series of closed-source models without bells and wistles, including GPT-4-1106, Claude-3-Opus, and Gemini-1.5-Pro.
- News
- Datasets
- Models
- Installation
- Training
- Evaluation
- Data Construction Pipeline
- Deployment
- Examples
- Acknowledgement
- Citation
- [x] [2024.7.7] We release the scripts for Data Construction Pipeline! You can construct dataset on your own with these scripts!
- [x] [2024.7.1] We release the demo of the model Qwen2-7B-Instruct-Step-DPO. Welcome to try it on Demo!
- [x] [2024.6.28] We release the pre-print of Step-DPO and this GitHub repo, including training/evaluation scripts, pre-trained models and data.
We build a 10K math preference datasets for Step-DPO, which can be downloaded from the following link.
| Dataset | Size | Link |
|---|---|---|
| xinlai/Math-Step-DPO-10K | 10,795 | 🤗 Hugging Face |
It is notable that the model Qwen2-72B-Instruct + Step-DPO could achieve 70.8% and 94.0% on MATH and GSM8K test sets. Step-DPO also brings considerable improvement over various models as follows. Welcome to download and use.
| Models | Size | MATH | GSM8K | Odyssey-MATH | Link |
|---|---|---|---|---|---|
| Qwen2-7B-Instruct | 7B | 53.0 | 85.5 | - | - |
| Qwen2-7B-Instruct + Step-DPO | 7B | 58.6 (+5.6) | 87.9 (+2.4) | - | 🤗 HF |
| DeepSeekMath-RL | 7B | 51.7 | 88.2 | - | - |
| DeepSeekMath-RL + Step-DPO | 7B | 53.2 (+1.5) | 88.7 (+0.5) | - | 🤗 HF |
| Qwen2-7B-SFT | 7B | 54.8 | 88.2 | - | 🤗 HF |
| Qwen2-7B-SFT + Step-DPO | 7B | 55.8 (+1.0) | 88.5 (+0.3) | - | 🤗 HF |
| Qwen1.5-32B-SFT | 32B | 54.9 | 90.0 | - | 🤗 HF |
| Qwen1.5-32B-SFT + Step-DPO | 32B | 56.9 (+2.0) | 90.9 (+0.9) | - | 🤗 HF |
| Qwen2-57B-A14B-SFT | 57B | 54.6 | 89.8 | - | 🤗 HF |
| Qwen2-57B-A14B-SFT + Step-DPO | 57B | 56.5 (+1.9) | 90.0 (+0.2) | - | 🤗 HF |
| Llama-3-70B-SFT | 70B | 56.9 | 92.2 | - | 🤗 HF |
| Llama-3-70B-SFT + Step-DPO | 70B | 59.5 (+2.6) | 93.3 (+1.1) | - | 🤗 HF |
| Qwen2-72B-SFT | 72B | 61.7 | 92.9 | 44.2 | 🤗 HF |
| Qwen2-72B-SFT + Step-DPO | 72B | 64.7 (+3.0) | 93.9 (+1.0) | 47.0 (+2.8) | 🤗 HF |
| Qwen2-72B-Instruct | 72B | 69.4 | 92.4 | 47.0 | - |
| Qwen2-72B-Instruct + Step-DPO | 72B | 70.8 (+1.4) | 94.0 (+1.6) | 50.1 (+3.1) | 🤗 HF |
Note: Odyssey-MATH contains competition-level math problems.
conda create -n step_dpo python=3.10
conda activate step_dpo
pip install -r requirements.txt
We use Qwen2, Qwen1.5, Llama-3, and DeepSeekMath models as the pre-trained weights and fine-tune them with Step-DPO. Download based on your choices.
Note: models with '-SFT' are supervised fine-tuned by our 299K SFT data based on open-source base models. You could perform Step-DPO on either our SFT models or existing open-source instruct models.
Here is a script example to perform Step-DPO on Qwen/Qwen2-72B-Instruct:
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file accelerate_configs/deepspeed_zero3_cpu.yaml --mixed_precision bf16 \
--num_processes 8 \
train.py configs/config_full.yaml \
--model_name_or_path="Qwen/Qwen2-72B-Instruct" \
--data_path="xinlai/Math-Step-DPO-10K" \
--per_device_train_batch_size=2 \
--gradient_accumulation_steps=8 \
--torch_dtype=bfloat16 \
--bf16=True \
--beta=0.4 \
--num_train_epochs=4 \
--save_strategy='steps' \
--save_steps=200 \
--save_total_limit=1 \
--output_dir=outputs/qwen2-72b-instruct-step-dpo \
--hub_model_id=qwen2-72b-instruct-step-dpo \
--prompt=qwen2-boxedHere are script examples to evaluate fine-tuned models on both GSM8K and MATH test sets:
python eval_math.py \
--model outputs/qwen2-72b-instruct-step-dpo \
--data_file ./data/test/GSM8K_test_data.jsonl \
--save_path 'eval_results/gsm8k/qwen2-72b-instruct-step-dpo.json' \
--prompt 'qwen2-boxed' \
--tensor_parallel_size 8
python eval_math.py \
--model outputs/qwen2-72b-instruct-step-dpo \
--data_file ./data/test/MATH_test_data.jsonl \
--save_path 'eval_results/math/qwen2-72b-instruct-step-dpo.json' \
--prompt 'qwen2-boxed' \
--tensor_parallel_size 8
We release the scripts to construct the Step-DPO data, as shown in the data_pipeline/ directory. Please follow the instructions below.
cd Step-DPO
# Step 1: Error Collection
# Before executing, please set the MODEL_PATH, PRED_PATH, EVAL_PROMPT
bash data_pipeline/step1.sh
# Step 2: Locate Erroneous Step by GPT-4o
# Before executing, please set the OPENAI_BASE_URL, OPENAI_API_KEY
bash data_pipeline/step2.sh
# Step 3: Rectify by the model itself
# Before executing, please set the MODEL_PATH, EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH
bash data_pipeline/step3.sh
# Finally, Get the resulting dataset
# Before executing, please set the EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH
bash data_pipeline/merge.sh
For deployment, please directly use the following command:
python3 app.py --model_path_or_name xinlai/Qwen2-7B-Instruct-Step-DPO
This repository is based on alignment-handbook, DeepSeekMath, and MetaMath.
Many thanks for their efforts!
If you find this project useful in your research, please consider citing us:
@article{lai2024stepdpo,
title={Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs},
author={Xin Lai and Zhuotao Tian and Yukang Chen and Senqiao Yang and Xiangru Peng and Jiaya Jia},
journal={arXiv:2406.18629},
year={2024}
}
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