PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis

Chi Phan*, Tianyi Zhang*, Yufeng Wu, Qiaochu Xue, Jiajie Zhang, Linghan Cai, Zeyu Liu, Sudong Wang, Yueming Jin, Dan Hu

Overview

Overview of the proposed cross-scale benchmark construction and model optimization framework. (A) Expert-verified diagnostic paths link clinically relevant 10×, 40×, and 200× ROIs from the same WSI, providing scale-specific captions and cross-scale evidence anchors. (B) From these paths, we construct cross-scale semantic reasoning and visual grounding tasks, with adversarial text-only screening to reduce language shortcuts and structure-controlled distractor sampling to reduce superficial visual shortcuts. (C) PathScale-R1 is optimized by difficulty- driven reasoning distillation followed by reinforcement learning with accuracy, format, and scale-aware reasoning structure rewards.

Dataset statistics and benchmark performance. (A) PathScale-VQA component statistics and organ distribution. (B) Task-wise performance of representative VLMs across single-scale and proposed cross-scale VQA benchmark.

Training Dynamics:

Repository Structure

PathScale-R1/
├── preprocess/
│   ├── generate_vqa_data/           # Cross-scale VQA generation and split creation
│   └── prompts/                     # Prompt templates and task constraints
├── script/
│   ├── preprocess/                  # Preprocessing entrypoints
│   ├── train/                       # SFT and GRPO training scripts
│   └── postprocess/                 # Checkpoint export / merge scripts
├── LLaMA-Factory/                   # SFT framework
├── verl/                            # RL framework
└── README.md        

Getting Started

Environment Setup

git clone [repo url placeholder]
cd PathScale-R1

cp script/.env.example script/.env
# Edit script/.env with your local paths and API keys

Configure script/.env:

# Data processing
DATA_DIR=/path/to/triplet_raw_data
ROOT=/path/to/PathScale-R1
PROCESSED_DIR=/path/to/processed_data

# Training
ACTOR_MODEL_DIR=/path/to/base_pathor1_model
LOG_DIR=/path/to/logs
WANDB_DIR=/path/to/wandb_logs
RESULTS_DIR=/path/to/results_dir

# Exporting checkpoints
RAW_CKPT_DIR=/path/to/raw_ckpt_dir
OUR_MODEL_DIR=/path/to/our_final_model_dir

# Secrets
GEMINI_API_KEY=
OPENAI_API_KEY=
DASHSCOPE_API_KEY=
HF_TOKEN=

Dependencies

This repo currently uses two training stacks:

• verl for GRPO-based RL training
• LLaMA-Factory for SFT

Suggested setup:

conda create -n verl python=3.10 -y
conda activate verl
pip install -e verl/

conda create -n sft python=3.10 -y
conda activate sft
pip install -e LLaMA-Factory/

CUDA / PyTorch version requirements should be finalized against the exact training environment used for release.

• Model download link: [TODO]
• Recommended inference backend: [TODO]
• System prompt / output format: [TODO]
• Evaluation script: [TODO]

🙏 Acknowledgements

This work was supported by the Ministry of Education, Singapore, under the Tier 1 grant (24-1250-P0001) and Tier 2 grant (T2EP20224-0028), and by PuzzleLogic Pte Ltd, Singapore.

We gratefully acknowledge the open-source projects that made the development of PathScale-R1 possible:

• verl, for the reinforcement learning training framework.
• LLaMA-Factory, for the unified fine-tuning pipelines.
• vLLM, for efficient large language model inference and serving.

We also acknowledge the following open-source models used for comparison in our experiments: Qwen2.5-VL-7B, Qwen3-VL-8B, InternVL3.5-8B, MiMo-VL-7B, LLaVA-Med-7B, HuatuoGPT-Vision-7B, MedVLThinker-7B, QoQ-Med-VL-7B, Lingshu-7B, HealthGPT-8B, OctoMed-7B, Quilt-LLaVA, CLOVER, and Patho-R1.

We sincerely thank the developers and contributors of these projects for their excellent work and for making their code and models publicly available to the research community.

❤️ Citation

If you find our work helpful, please consider citing our paper and the frameworks we build upon:

@article{phan2026pathscale,
  title={PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis},
  author={Phan, Chi and Zhang, Tianyi and Wu, Yufeng and Xue, Qiaochu and Zhang, Jiajie and Cai, Linghan and Liu, Zeyu and Wang, Sudong and Jin, Yueming and Hu, Dan},
  journal={arXiv preprint arXiv:2607.23794},
  year={2026}
}