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‎README.md‎

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# GraphPRM
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# GraphPRM: Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners
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Code and data for KDD 2025 Research Track Anonymous Submission: "Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners"
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<div align="left">
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<p>
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<a href='https://arxiv.org/abs/2503.00845'><img src='https://img.shields.io/badge/arXiv-2503.00845-b31b1b'></a>
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<a href='https://huggingface.co/datasets/GraphPRM/GraphSilo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-GraphSilo-blue'></a>
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<a href='https://huggingface.co/GraphPRM/GraphPRM-7B'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-GraphPRM-purple'></a>
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<a href='https://github.com/GKNL/GraphPRM'><img src='https://img.shields.io/badge/GitHub-GraphPRM-green'></a>
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</p>
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</div>
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## Dataset and Model Weight Link
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**GraphPRM** is the first Process Reward Model tailored for graph reasoning tasks, which further enhancing LLMs' mathematical reasoning capabilities on other reasoning domains, including mathematical problem-solving tasks. We also developed **GraphSilo**, the largest dataset for graph reasoning with fine-grained CoT solutions, with 118,189 samples and 394,165 step-wise labels.
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**Full dataset can also be accessed at:** [GraphSilo](https://huggingface.co/datasets/GraphPRM/GraphSilo), [GraphSilo-Test](https://huggingface.co/datasets/GraphPRM/GraphSilo-Test) (Anonymous Repository)
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This repository contains the code and data for training and evaluating GraphPRM models, along with the full GraphSilo dataset. Please check our [paper](https://arxiv.org/abs/2503.00845) for more details.
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**Full GraphPRM model weight can be accessed at:** [GraphPRM-1.5B](https://huggingface.co/GraphPRM/GraphPRM-1.5B), [GraphPRM-7B](https://huggingface.co/GraphPRM/GraphPRM-7B) (Anonymous Repository)
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<p align="center">
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<img src="image/overview.jpg" width="800px"/>
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</p>
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## Key File Descriptions
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## 💫 News
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### `data/`
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- **[2025.05.15]** GraphPRM is accepted to **KDD 2025 Research Track**. 🔥🔥🔥
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- **[2025.02.15]** Initial release of 🤗[GraphSilo](https://huggingface.co/datasets/GraphPRM/GraphSilo) dataset and 🤗[GraphPRM](https://huggingface.co/GraphPRM/GraphPRM-7B) models. 🚀🚀🚀
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- `GraphSilo/`: Training set for GraphPRM model (containing step-wise labels from "Task-oriented Trajectories" and "Monte Carlo Estimation").
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## 📊 Dataset and Models
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- `GraphSilo_test/`: Test set of 13 graph tasks in GraphSilo.
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- `[graph_task].jsonl`: Test samples for corresponding graph tasks.
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- `GraphSilo_test_in_domain.jsonl`: Test samples for 10 in-domain graph tasks (that used to train GraphPRM): Degree, Clustering Coefficient, Jaccard, Common Connectivity, Diameter, Page Rank, MST, Maximum Flow, Predecessor.
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- `GraphSilo_test_out_domain.jsonl`: Test samples for 3 out-domain graph tasks (that not used to train GraphPRM): BFS, Neighbor, Cycle.
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- `GraphSilo_test.jsonl`: All test samples including 13 graph tasks.
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The full GraphSilo dataset and GraphPRM models can be accessed at:
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### `prm/`
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- **GraphSilo Dataset**: [GraphSilo](https://huggingface.co/datasets/GraphPRM/GraphSilo), [GraphSilo-Test](https://huggingface.co/datasets/GraphPRM/GraphSilo-Test)
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- **GraphPRM Models**: [GraphPRM-1.5B](https://huggingface.co/GraphPRM/GraphPRM-1.5B), [GraphPRM-7B](https://huggingface.co/GraphPRM/GraphPRM-7B)
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- `code/finetune_qwen_SFT.py`: Codes for SFT training GraphPRM with step-wise labels from GraphSilo.
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- `config/deepspeed_config_stage3.json`: Configuration for deepspeed stage3 training.
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## 📦 Installation
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### `reason/`
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- `llm_service/create_service_graph.sh`: Script to start LM and RM services.
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### `scripts/`
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- `eval/best_of_N.sh`: Perform inference-time computation via Best-of-N strategy with GraphPRM.
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- `eval/beam_search.sh`: Perform inference-time computation via Beam Search strategy with GraphPRM.
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## Usage Instructions
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### Installation
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```
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```bash
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conda create -n GraphPRM python=3.10
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conda activate GraphPRM
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pip install -r requirements.txt
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pip3 install "fschat[model_worker,webui]"
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pip3 install "fschat[model_worker,webui]"
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pip install -U pydantic
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cd envs/MATH/latex2sympy
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pip install -e .
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cd -
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```
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## 🛠️ Usage
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### Download Models
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Before running the project, please ensure that all required base models are downloaded to directory `hugging_cache`.
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1. Download base LLM models: `Qwen2.5-1.5B-Instruct, Qwen2.5-7B-Instruct, Qwen2.5-Math-7B-Instruct, LLaMA3.1-8B-Instruct, Gemma2-9B-Instruct`
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2. Download GraphPRM models: `GraphPRM-7B`
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To download these models, please refer to the [Hugging Face model downloading tutorial](https://huggingface.co/docs/hub/models-downloading) for step-by-step guidance on downloading models from the Hugging Face Hub.
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### Start LM & RM Services
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Before running inference, please modify the following variables in the script at `reason/llm_service/create_service.sh` to set the appropriate base models:
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- `$MODEL_BASE`: Set this to the directory where the models are stored.
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- `$POLICY_MODEL_NAME`: Set this to the name of the policy model.
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- `$VALUE_MODEL_NAME`: Set this to the name of the graph reward model.
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- `$NUM_LM_WORKER`: Set this to the number of language model (LM) workers to start.
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- `$NUM_RM_WORKER`: Set this to the number of reward model (RM) workers to start.
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1. Modify the following variables in `reason/llm_service/create_service.sh`:
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- `$MODEL_BASE`: Directory where models are stored
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- `$POLICY_MODEL_NAME`: Name of the policy model
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- `$VALUE_MODEL_NAME`: Name of the graph reward model
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- `$NUM_LM_WORKER`: Number of language model workers
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- `$NUM_RM_WORKER`: Number of reward model workers
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Then it prepares and runs inference using different techniques.
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For example, to start the LM and RM services for scaling inference-time computing with GraphPRM, run the following command:
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2. Start the services:
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```bash
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sh reason/llm_service/create_service.sh
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```
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To kill the server processes, recommend using the following command:
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3. To stop the services:
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```bash
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tmux kill-session -t {Your Session Name} # default is `GraphPRM`
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```
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### Run GraphPRM Self-supervised Finetuning
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### Training GraphPRM
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```bash
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cd prm/code
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CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 finetune_qwen_SFT.py
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--model_path $YOUR_MODEL_PATH \
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--data_path $YOUR_DATA_FOLDER_PATH
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CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 finetune_qwen_SFT.py \
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--model_path $YOUR_MODEL_PATH \
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--data_path $YOUR_DATA_FOLDER_PATH
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```
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### Perform Inference-time Computation with GraphPRM
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### Inference Methods
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#### Best-of-N
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#### Best-of-N Strategy
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```bash
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export PYTHONPATH=$(pwd)
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sh scripts/eval/cot_rerank.sh
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# Key parameters:
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# --LM Qwen2.5-7B-Instruct # The name of Policy Model
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# --RM GraphPRM-7B # The name of Reward Model
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# --temperature 0.7 # The temperature hyper-parameter during generation
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# --num_sequence 8 # The number of generated samples during generation
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# --max_new_tokens 2048 # Max new token number during generation
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# --test_set_path dataset/GraphSilo_test.jsonl # The path to test data file
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# --LM Qwen2.5-7B-Instruct # Policy Model name
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# --RM GraphPRM-7B # Reward Model name
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# --temperature 0.7 # Generation temperature
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# --num_sequence 8 # Number of generated samples
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# --max_new_tokens 2048 # Max new tokens
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# --test_set_path dataset/GraphSilo_test.jsonl # Test data path
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```
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#### Beam Search
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#### Beam Search Strategy
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```bash
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export PYTHONPATH=$(pwd)
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sh scripts/eval/beam_search.sh
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# Key parameters:
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# --LM Qwen2.5-7B-Instruct # The name of Policy Model
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# --RM GraphPRM-7B # The name of Reward Model
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# --temperature 0.7 # The temperature hyper-parameter during generation
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# --num_sequence 2 # The number of samples to remain per step
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# --tree_max_width 4 # The number of generated samples per step during generation
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# --tree_max_depth 50 # Max step number
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# --max_new_tokens 2048 # Max new token number during generation
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# --test_set_path dataset/GraphSilo_test.jsonl # The path to test data file
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# --LM Qwen2.5-7B-Instruct # Policy Model name
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# --RM GraphPRM-7B # Reward Model name
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# --temperature 0.7 # Generation temperature
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# --num_sequence 2 # Samples per step
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# --tree_max_width 4 # Generated samples per step
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# --tree_max_depth 50 # Max steps
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# --max_new_tokens 2048 # Max new tokens
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# --test_set_path dataset/GraphSilo_test.jsonl # Test data path
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```
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## 📁 Project Structure
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```
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GraphPRM/
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├── data/
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│ ├── GraphSilo/
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│ │ ├── train.jsonl
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│ │ └── step_wise_labels.jsonl
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│ └── GraphSilo_test/
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│ ├── in_domain/
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│ │ ├── degree.jsonl
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│ │ ├── clustering_coefficient.jsonl
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│ │ ├── jaccard.jsonl
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│ │ └── ...
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│ └── out_domain/
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│ ├── bfs.jsonl
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│ ├── neighbor.jsonl
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│ └── cycle.jsonl
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├── prm/
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│ ├── code/
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│ │ └── finetune_qwen_SFT.py
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│ └── config/
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│ └── deepspeed_config_stage3.json
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├── reason/
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│ └── llm_service/
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│ └── create_service_graph.sh
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└── scripts/
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└── eval/
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├── best_of_N.sh
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└── beam_search.sh
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```
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### Key Components
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- **data/**: Contains the GraphSilo dataset
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- `GraphSilo/`: Training set with step-wise reasoning trajectories
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- `GraphSilo_test/`: Test set for 13 graph tasks
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- In-domain tasks (10): Degree, Clustering Coefficient, Jaccard, etc.
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- Out-domain tasks (3): BFS, Neighbor, Cycle
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- **prm/**: Process Reward Modeling related code
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- `code/`: SFT training code
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- `config/`: DeepSpeed configuration files for training
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- **reason/**: Reasoning service implementation
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- `llm_service/`: Service startup and management scripts
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- **scripts/**: Evaluation and utility scripts
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- `eval/`: Inference scripts for different strategies
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## Acknowledge
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Some code implementations are built upon [OpenR](https://github.com/openreasoner/openr) Repository. We sincerely appreciate the efforts for their contributions.
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## 📜 Citation
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If you find GraphPRM useful for your research and applications, please kindly cite using this BibTeX:
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```
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@misc{graphprm,
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title={Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners},
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author={Miao Peng and Nuo Chen and Zongrui Suo and Jia Li},
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year={2025},
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eprint={2503.00845},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2503.00845},
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}
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```

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