AwaRes Training Dataset

Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs

Paper | Project Page | GitHub

This is the training dataset for AwaRes — a spatial-on-demand VLM inference framework that processes low-resolution images first and selectively retrieves high-resolution crops via tool-calling. AwaRes achieves comparable performance to Qwen2.5-VL-7B across six benchmarks while using only ~36% of the visual tokens, offering a 4.4× speedup over dynamic high-resolution methods.

Dataset Description

The dataset contains ~48.7K visual question-answering samples with oracle crop annotations, curated for training VLMs to learn a coupled decision policy: whether high-resolution detail is needed and where to acquire it.

Columns

Column	Type	Description
image	list[Image]	Low-res and high-res crop images
problem	string	VQA question posed to the model
solution	string	Ground-truth answer
original_question	string	Original question before prompt formatting
original_answer	string	Original answer
hr_helps	int	Binary label — whether high-resolution crops help answer the question
helped_crops	list	Which crop indices were useful
image_paths	list	Original file paths for the crop images
modified	int	Whether the sample instruction was rephrased during curation

Usage

from datasets import load_dataset

ds = load_dataset("NimrodShabtay1986/AwaRes")

Data Curation Pipeline

Samples were constructed automatically via a three-stage pipeline:

1. Resolution comparison: Qwen2.5-VL-7B generates answers at both low and high resolution. A LLaMA-3.3-70B judge compares outputs to label whether high-resolution crops help (hr_helps).
2. Oracle grounding: For samples where HR helps, an oracle grounding model localizes the visual evidence for the correct answer, which is mapped to a discrete crop set.
3. Trajectory construction: Labels are converted into multi-turn tool-use trajectories for SFT cold-start training, followed by multi-turn GRPO with composite rewards (semantic correctness + crop-cost penalty).

Citation

@article{shabtay2026look,
  title={Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs},
  author={Shabtay, Nimrod and Kimhi, Moshe and Spector, Artem and Haray, Sivan and Rivlin, Ehud and Baskin, Chaim and Giryes, Raja and Schwartz, Eli},
  journal={arXiv preprint arXiv:2603.16932},
  year={2026}
}

Links

• arXiv Paper
• Project Page
• GitHub