inclusionAI/ZoomGainBench

📈 ZoomGainBench results

Scores are percentages. Each language's overall score is weighted by the number of questions in each category. Bold marks the best result in each column. ZoomGainBench results in the paper (Table 2)

Model	EN Attr.	EN Count	EN OCR	EN Overall	ZH Attr.	ZH Count	ZH OCR	ZH Overall	Overall
Closed-source Frontier VLMs
GPT-5.5	52.75	57.45	60.40	56.51	52.12	63.83	60.96	57.06	56.79
Gemini-3.6-Flash	51.27	42.55	74.71	60.62	48.52	45.74	71.42	58.20	59.41
Claude-Opus-5	48.09	63.83	71.86	59.89	48.73	62.77	71.29	59.84	59.86
GPT-5.6-Sol	56.57	62.77	67.93	62.08	54.03	63.83	65.84	60.07	61.08
Agentic VLMs
DeepEyes-7B	24.79	20.21	41.05	31.42	24.36	22.34	34.46	28.55	29.99
Thyme-7B	26.69	34.04	43.88	34.85	22.88	31.91	40.31	31.30	33.07
DeepEyesV2-7B	22.25	41.49	43.70	33.37	22.67	34.04	43.62	32.83	33.10
PixelReasoner-7B	27.12	45.74	44.83	36.56	25.42	23.40	40.13	31.62	34.09
Fine-grained Enhanced Single Forward VLMs
ZwZ-4B	30.08	39.36	29.68	30.78	33.47	50.00	34.45	35.45	33.12
Vision-OPD-4B	35.59	59.57	45.69	42.23	39.62	59.57	43.80	43.31	42.77
Vision-OPD-9B	46.40	55.32	47.18	47.58	47.67	60.64	37.97	44.68	46.13
ZwZ-8B	42.58	55.32	50.53	47.23	44.70	52.13	51.77	48.47	47.85
Open-source General VLMs
MiMo-VL-7B-RL	23.73	34.04	20.06	23.11	23.31	41.49	32.06	28.82	25.96
GLM-4.6V (106B)	24.36	38.30	31.57	28.80	22.46	36.17	28.51	26.37	27.59
Qwen3-VL-4B	21.82	43.62	31.14	27.92	22.88	41.49	33.63	29.30	28.61
Qwen3-VL-8B	15.89	41.49	39.17	28.40	19.92	30.85	41.64	30.37	29.38
Qwen3.5-4B	21.40	51.06	34.40	29.83	22.67	46.81	34.87	30.23	30.03
Qwen3.5-9B	21.82	41.49	37.79	30.60	22.67	47.87	42.52	33.66	32.13
Qwen3.5-35B-A3B	28.18	44.68	46.10	37.51	27.33	51.06	47.95	38.51	38.01
Qwen3.6-35B-A3B	26.91	54.26	50.21	39.59	26.69	52.13	46.17	37.54	38.57
Qwen3-VL-235B-A22B	29.45	52.13	48.01	39.64	30.93	50.00	50.90	41.39	40.51
Qwen3.5-27B	33.05	51.06	45.92	40.33	34.75	58.51	48.67	43.02	41.68
Qwen3.6-27B	33.47	56.38	47.47	41.70	37.71	70.21	45.33	44.07	42.89
Qwen3.8-27B	40.25	54.26	45.49	43.84	44.92	62.77	43.05	45.78	44.81
Qwen3.5-397B-A17B	33.47	65.96	51.95	44.55	38.14	62.77	50.41	45.78	45.16
GLM-5.3-Flash (320B)	44.07	54.26	43.77	44.90	46.61	55.32	43.74	46.18	45.54
Kimi-K3 (2.8T)	49.15	64.89	45.48	49.04	46.82	64.89	46.22	48.26	48.65
Qwen3.8-Flash-Next (125B)	47.46	67.02	56.97	53.42	52.97	67.02	58.45	56.67	55.05

🖼️ Benchmark comparison

Comparison of benchmark design in the paper (Table 1)

🔍 ZoomGain

Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision

💻 GitHub  ·  📄 Paper  ·  📊 ZoomGainBench  ·  🤗 RL Data  ·  🧠 ZoomGain-4B  ·  🧠 ZoomGain-9B

ZoomGain builds questions about a full image from visual details that become clear when you zoom in. We use this global-to-zoom perception gap to construct supervision for vision-language models. In the paper, the best of 28 models scored 61.08 on ZoomGainBench, and ZoomGain-9B reached superior performance on multiple fine-grained perception and general perception benchmarks. Paper

📜 Updates

• 2026.11.11 🌟 We released the arXiv paper.
• 2026.11.11 🌟 We released the evaluation benchmark and RL training dataset. ZoomGainBench · ZoomGain RL Data
• 2026.11.11 🌟 We released the model checkpoints. ZoomGain-4B · ZoomGain-9B

🔬 How ZoomGain Works

1. Ground the target. Multiple models independently locate a target described in the full scene. Their agreement identifies a region to crop.
2. Mine evidence. Compare captions of the same region at both scales, then turn details revealed by the crop into questions about the full image.
3. Verify questions and answers. Cross-check answers on the crop, filter ambiguous questions and questions answerable without the image, and build plausible distractors for multiple-choice items.

🛠️ Setup

Clone the repository and set an asset directory. The commands below assume you run them from the repository root.

git clone https://github.com/inclusionAI/ZoomGain.git
cd ZoomGain

export ASSET_DIR="$PWD/zoomgain-assets"
mkdir -p "$ASSET_DIR"
python -m pip install -U huggingface_hub

Run hf auth login if the Hugging Face repositories require authenticated access. Hugging Face CLI guide

🧰 Install inference and evaluation dependencies

python -m pip install -r eval_ZoomGainBench/requirements.txt \
  'vllm==0.19.1' 'transformers==5.7.0'

📊 Evaluate on ZoomGainBench

📦 Download the benchmark

ZoomGainBench contains 1,000 questions over 941 images covering attribute recognition, counting, and OCR. Each annotation includes English and Chinese question text, a reference answer, scoring rules, and an evidence bounding box in image pixel coordinates.

BENCH_DIR="$ASSET_DIR/data/ZoomGainBench"
hf download inclusionAI/ZoomGainBench annotations.jsonl images.zip \
  --repo-type dataset --local-dir "$BENCH_DIR"
unzip -q "$BENCH_DIR/images.zip" -d "$BENCH_DIR"

Keep images/ next to annotations.jsonl. The evaluator loads the full image from each annotation's relative image_path and uses evidence_bbox to validate the evidence location.

🚀 Run evaluation

Install the dependencies from the model section and run the benchmark from the repository root. Replace the example MODEL_PATH with the path to a local checkpoint directory for any compatible model.

MODEL_PATH="local/path/to/Qwen3.5-4B" \
ANNOTATIONS="$BENCH_DIR/annotations.jsonl" \
OUTPUT_DIR="outputs/qwen3.5-4b-bench" \
GPUS=0 PORT=18811 LANGUAGE=all \
bash eval_ZoomGainBench/eval.sh

• LANGUAGE=all evaluates both languages for 2,000 tasks. Use LANGUAGE=en for English or LANGUAGE=cn for Chinese.
• GPUS=0,1 with TP=1 runs two independent replicas. Set TP=2 to use both GPUs for one replica.
• Append --dry-run to validate the model and dataset or --limit 10 for a short GPU run. Evaluation defaults to temperature 0 with Qwen3.5 thinking disabled.

Read report.md for the evaluation scores, summary.json for aggregate metrics, and results.json for per-question results. responses.jsonl contains the raw model responses.

🎯 Train on ZoomGain RL Data

📦 Download the training data

The release contains 9,000 training records and 7,713 source images. The Parquet stores prompts, relative image paths, reference answers, and metadata used by the reward function.

RL_DIR="$ASSET_DIR/data/ZoomGain-RL-Data"
hf download inclusionAI/ZoomGain-RL-Data train.parquet images.zip \
  --repo-type dataset --local-dir "$RL_DIR"
unzip -q "$RL_DIR/images.zip" -d "$RL_DIR"

The training launcher resolves image paths relative to train.parquet and gives EasyR1 a temporary Parquet with absolute paths. Keep the extracted images/ directory next to train.parquet.

ZoomGain-RL-Data/
├── train.parquet
└── images/
    ├── image_000001.jpg
    └── ...

⚙️ Install the training environment

Use a Python 3.10 environment with a CUDA toolkit and a C++ compiler for any CUDA extensions that need to build locally. The example below uses the CUDA 12.8 PyTorch wheels. PyTorch wheel index

python -m pip install torch==2.10.0 torchvision==0.25.0 \
  --index-url https://download.pytorch.org/whl/cu128
python -m pip install packaging psutil ninja
python -m pip install --no-build-isolation flash-attn==2.8.3 causal-conv1d==1.6.2.post1
python -m pip install -r requirements.txt

The launch scripts load the bundled EasyR1 code directly. Point BASE_MODEL at the original Qwen3.5 checkpoint to start RL training from the same model family used in the paper. Qwen3.5-4B · Qwen3.5-9B

🧪 Validate and run a short training job

hf download Qwen/Qwen3.5-4B --local-dir "$ASSET_DIR/models/Qwen3.5-4B"

python zoomgain/prepare_data.py --input "$RL_DIR/train.parquet"

BASE_MODEL="$ASSET_DIR/models/Qwen3.5-4B" \
TRAIN_PARQUET="$RL_DIR/train.parquet" \
OUTPUT_DIR="$PWD/outputs/train-smoke-4b" \
CUDA_VISIBLE_DEVICES=0,1 ROLLOUT_N=4 MAX_RESPONSE_LENGTH=512 ROLLOUT_AGENT_WORKERS=2 \
bash scripts/smoke_train.sh

The smoke test runs one GRPO step, including rollout generation, reward calculation, and a checkpoint save.

🏋️ Launch a full training run

BASE_MODEL="$ASSET_DIR/models/Qwen3.5-4B" \
TRAIN_PARQUET="$RL_DIR/train.parquet" \
OUTPUT_DIR="outputs/train-zoomgain-4b" \
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
bash scripts/train_zoomgain_4b.sh

For 9B, download Qwen/Qwen3.5-9B to a local directory, update BASE_MODEL and OUTPUT_DIR, and run scripts/train_zoomgain_9b.sh. The full launchers default to one epoch, a batch size of 64, and eight rollouts per question. The output directory contains training logs, rollouts, and checkpoints. To evaluate a saved model, set MODEL_PATH to <output-dir>/checkpoints/global_step_<N>/actor/huggingface.

📄 License

The code is released under the Apache License, Version 2.0. The bundled training framework retains its upstream license and notices. See the Hugging Face cards for the terms of each dataset and checkpoint. LICENSE · NOTICE

⭐ Citation

If you find this repository helpful to your research, welcome to cite our paper:

@article{shen2026zoomgain,
  title   = {ZoomGain: Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision},
  author  = {Shen, Haozhan and He, Liangbo and Lan, Jun and Zhu, Huijia and Wang, Weiqiang and Zhao, Tiancheng and Yin, Jianwei},
  journal = {arXiv preprint arXiv:2611.14076},
  year    = {2026}
}