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蚂蚁 inclusionAI：HuggingFace 新模型·· 22 小时前AI 评分42

蚂蚁 inclusionAI 开源 ZoomGain-4B/9B 模型与 ZoomGainBench 基准

inclusionAI/ZoomGain-4B

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蚂蚁 inclusionAI 发布 ZoomGain-4B 与 ZoomGain-9B 模型检查点，并同步开源 ZoomGainBench 评测基准和 RL 训练数据。

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📈 Results of RL training on ZoomGain and ZwZ data

Table 3 showing RL training results for Qwen3.5-4B and Qwen3.5-9B on ZoomGain and ZwZ data

Results from Table 3 of the paper

📥 Download ZoomGain-4B 

hf download inclusionAI/ZoomGain-4B --local-dir "./ZoomGain-4B"

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

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

ZoomGain uses zoomed-in details to build questions about the full image, producing a challenging benchmark and training data that improves fine-grained perception.

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

🔬 How ZoomGain Works

Three stages of ZoomGain data construction: anchor grounding, global-to-zoom evidence mining, and answer verification with multiple-choice distractors.

  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}
}

来源：蚂蚁 inclusionAI：HuggingFace 新模型 · huggingface.co

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