Euclid30K Dataset

Paper | Project Page | Code

Spatial intelligence spans a rich suite of abilities, including visualising and transforming shapes, mentally rotating objects, judging relational positions and containment, and estimating numerosity.

However, it still remains a critical unresolved challenge for Multimodal Large Language Models (MLLMs).

To fill this gap, we propose to treat Euclidean geometry problem-solving as a surrogate task. Specifically, we meticulously constructed a curated multimodal dataset, called Euclid30K, comprising approximately 30K plane and solid geometry problems.

To enable the model to acquire and apply Euclidean principles from these geometry problems, we employed GRPO to finetune the Qwen2.5VL family and RoboBrain2.0 family, inspiring the models to identify shapes, count, and relate entities, and perform multi-step deductive reasoning using Euclidean principles.

Our experiments demonstrate that the resulting models achieve substantial zero-shot gains across four spatial reasoning benchmarks (Super-CLEVR, Omni3DBench, VSI‑Bench, and MindCube) without any task-specific adaptations. Notably, after training on the Euclid30K, the mean VSI‑Bench accuracy of all evaluated models rose from 34.5% to 40.5%, improving by 5.5 percentage points. Among them, RoboBrain2.0-Euclid‑7B achieves 49.6% accuracy, surpassing the previous state‑of‑the‑art model, Spatial‑MLLM.

To our knowledge, this is the first systematic study showing that geometry-centric fine-tuning can confer vision-language models with broadly transferable spatial skills.

Sample Usage

Below are instructions and code snippets for setting up the environment, training, and evaluation, adapted from the official GitHub repository.

1) Environment Setup

Training

• Install EasyR1 following the official documentation.
• Install the required Python dependencies:

  pip install -r requirements.txt
  

• Download the Euclid30K dataset from Hugging Face: https://huggingface.co/datasets/LiamLian0727/Euclid30K

Evaluation

• Install lmms‑eval following its official documentation. You can either:
  • Use the lmms-eval/ copy included in the GitHub repository; or
  • Copy the four task folders provided under test/lmms_eval/tasks/ from the GitHub repository into your existing lmms‑eval setup.
• Download the benchmark datasets Super‑CLEVR, Omni3DBench, VSI‑Bench, and MindCube_lmms_eval; then update the dataset paths in each corresponding YAML under test/lmms_eval/tasks/.

2) Training

Below is an example command for training (e.g., 8 GPUs). For multi‑node multi‑GPU training, refer to the example script train/dist_train.sh in the GitHub repository.

python3 -m verl.trainer.main \
    config=examples/config.yaml \
    data.train_files=/mnt/datasets/Euclid30K/Euclid30K_train.parquet \
    data.val_files=/mnt/datasets/Euclid30K/Euclid30K_val.parquet \
    worker.actor.model.model_path=/mnt/models/Qwen2.5-VL-7B-Instruct \
    trainer.experiment_name=EXPERIMENT_NAME \
    worker.actor.micro_batch_size_per_device_for_update=1 \
    worker.actor.micro_batch_size_per_device_for_experience=8 \
    worker.actor.clip_ratio_low=0.2 \
    worker.actor.clip_ratio_high=0.28 \
    worker.reward.reward_function=/mnt/code/Euclids_Gift/train/euclid.py:compute_score \
    algorithm.online_filtering=True \
    trainer.total_epochs=10 \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=2 \
    trainer.save_checkpoint_path=/mnt/models/Qwen2.5-VL-7B-Euclid

3) Evaluation

Use test/eval_qwen.sh, test/eval_robo.sh, and test/eval_euclid.sh from the GitHub repository to evaluate the Qwen2.5‑VL series, the RoboBrain 2.0 series, and Euclid models trained on Euclid30K, respectively.

Before running these scripts, set model_path in each script to the path of the model you want to evaluate.

Citation

If you find our dataset useful for your research, please cite us:

@misc{Euclids_Gift,
    title={Euclid’s Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks},
    author={Shijie Lian and Changti Wu and Laurence Tianruo Yang and Hang Yuan and Bin Yu and Lei Zhang and Kai Chen},
    year={2025},
    eprint={2509.24473},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2509.24473}
}