This is the main 9B VAD model trained with the symmetric Jensen--Shannon divergence (JSD) objective. It is the global_step_70 checkpoint reported in the paper, with an unweighted six-benchmark average of 79.93 under the Vision-OPD official evaluation pipeline and GPT-OSS-120B judge.

Model description

Multimodal on-policy distillation normally transfers the complete correction proposed by a privileged teacher. That correction can mix visual evidence with language preferences, formatting behavior, and other teacher-specific effects. VAD instead asks which part of the correction is attributable to a controlled change in the relevant visual evidence.

At each student-generated prefix, VAD evaluates the same fixed teacher with the evidence present and removed. The resulting distributional response provides a signed proxy direction for visual support and refutation. VAD projects the teacher correction onto this direction, allocates separate budgets to supported and refuted candidate tokens, and reconstructs a target around the full-image student distribution. The reconstructed target supplies the primary JSD supervision, while the original privileged teacher contributes only a weak stability regularizer.

The auxiliary teacher views are used only during training. Inference requires a single image and uses the standard Qwen3.5-9B architecture.

Checkpoint identity

Item	Value
Base model	`Qwen/Qwen3.5-9B`
Internal run	`VC-OPD-Qwen35-9B-BASVCtau07-fixedTeacher-bs96-mb96-eta01-gpu030-eval15-ctx9216-2epoch-step130-restart-20260628_183144`
Released checkpoint	`global_step_70`
Primary supervision	Symmetric JSD, $\alpha=0.5$
VAD target	Branch-separated, budgeted visual support and refutation
Positive-branch cap	$\tau_+=0.7$
Weak teacher-regularizer weight	$\lambda=0.1$
Projection stabilizer	$\zeta=10^{-3}$
Coordinate shift bound	$c=20$
Teacher	Frozen copy of the initial Qwen3.5-9B student
Training schedule	2 epochs / 130 steps; checkpoint selected at step 70
Learning rate	$2\times10^{-6}$
Train batch size	96
Rollouts per prompt	8
Maximum prompt / response length	8192 / 1024 tokens
Training context length	9,216 tokens
Training GPUs	8
Random seed	42

Training used 6,241 synthetic visual question-answering examples. Each example contains a full image, an evidence-present crop, and a spatially matched evidence-degraded crop. The primary token-level target is evaluated over the student top-100 support plus a tail bucket. During training, VStar, ZoomBench, HRBench-4K, and HRBench-8K were monitored every 15 optimization steps; the official results below were computed separately after training.

Evaluation

We evaluated this checkpoint using the Vision-OPD official inference and accuracy pipeline with openai/gpt-oss-120b as judge. Inference used max_model_len=32768, MAX_TOKENS=8192, ENABLE_THINKING=False, and seed 42. The judge used temperature 0. All answer and judge files passed the completeness audit, with no non-canonical final judge labels.

Benchmark	Accuracy
VStar	94.76
ZoomBench	62.49
HRBench-4K	87.88
HRBench-8K	85.75
MME-RealWorld-EN	76.56
MME-RealWorld-CN	72.13
Unweighted average	79.93

These results are specific to this checkpoint, benchmark versions, inference configuration, and judge protocol.

Serving with vLLM

Use a recent vLLM release with Qwen3.5 support:

bash

vllm serve zhangkangning/VAD_for_Qwen3.5-9b \
  --host 0.0.0.0 \
  --port 8000 \
  --served-model-name VAD-Qwen3.5-9B \
  --trust-remote-code \
  --tensor-parallel-size 2 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90

Adjust --tensor-parallel-size to the available hardware. A single high-memory GPU can use --tensor-parallel-size 1.

Example OpenAI-compatible multimodal request:

python

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="VAD-Qwen3.5-9B",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image.jpg"},
                },
                {
                    "type": "text",
                    "text": "Describe the fine-grained visual details.",
                },
            ],
        }
    ],
    max_tokens=1024,
    temperature=0,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)

print(response.choices[0].message.content)

Replace the placeholder image URL with an accessible image URL or a supported data URL.

Intended use and limitations

This checkpoint is intended for research on multimodal perception, fine-grained visual question answering, and on-policy distillation.
Performance outside the reported visual benchmarks, including broad language capability, safety, bias, and robustness, has not been comprehensively evaluated.
Like other vision-language models, it may produce incorrect or unsupported answers. Independently verify outputs in high-stakes settings.
The model inherits the capabilities and limitations of Qwen3.5-9B.

License

The checkpoint is released under the Apache License 2.0, consistent with the base model. Users must also comply with the Qwen3.5-9B license.

Citation

The public paper identifier and final citation will be added when available. No provisional bibliographic entry is provided here.