--- license: apache-2.0 base_model: - Qwen/Qwen3.5-4B language: - en - zh library_name: transformers pipeline_tag: image-text-to-text datasets: - yuanqianhao/Vision-OPD-6K tags: - multimodal - vision-language - fine-grained-visual-understanding - on-policy-distillation - self-distillation --- # Vision-OPD-4B

πŸ“„ Paper  |  πŸ’» Code  |  πŸ€— Training Data  |  πŸ“¦ Collection

## Model Summary **Vision-OPD-4B** is a multimodal LLM built on [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) and trained with **Vision-OPD (Vision On-Policy Distillation)**, the regional-to-global self-distillation framework introduced in [Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation](https://arxiv.org/abs/2605.18740). MLLMs often answer fine-grained questions correctly when given an evidence-centered **crop**, yet fail on the corresponding **full image** β€” a persistent *regional-to-global perception gap*. Vision-OPD closes this gap by letting the model act as its own teacher: a **crop-conditioned teacher** and a **full-image-conditioned student** are instantiated from the same MLLM, the student generates on-policy rollouts, and training minimizes the token-level divergence between the two distributions along these rollouts. The benefit of visual zooming is thereby internalized into a **single forward pass** β€” no external teacher, no ground-truth labels, no reward verifiers, and no inference-time tool use. With only **6.2K synthetic samples**, Vision-OPD-4B substantially improves over its Qwen3.5-4B base and outperforms much larger open-source models (e.g., Kimi-K2.6, GLM-4.6V), closed-source models (e.g., GPT-5.4, GPT-5.2), and "Thinking-with-Images" agentic methods on fine-grained visual understanding benchmarks, while preserving general multimodal abilities on holdout tasks. See also the larger [Vision-OPD-9B](https://huggingface.co/yuanqianhao/Vision-OPD-9B). ## Key Features - πŸ” **Sees fine details in one forward pass** β€” no cropping tools, zoom-in calls, or multi-step agentic inference at test time. - πŸͺž **Self-distillation, label-free** β€” the model's own crop-conditioned perception supervises its full-image policy; no external teacher, ground-truth labels, or reward verifiers. - 🎯 **On-policy + dense supervision** β€” token-level divergence (JSD, Ξ² = 0.5) on the student's own rollouts avoids the exposure bias of SFT and the sparse rewards of RLVR. - πŸ“‰ **Data-efficient** β€” trained on only 6.2K automatically synthesized samples ([Vision-OPD-6K](https://huggingface.co/datasets/yuanqianhao/Vision-OPD-6K)), one epoch. - 🧠 **No forgetting** β€” general multimodal capability is preserved on holdout tasks (MMVP, CV-Bench, MMStar, POPE). ## How It Works 1. **Data synthesis** β€” evidence regions are proposed on unlabeled images via object identification and segmentation; a question answerable from the crop alone is generated, and the region's bounding box is overlaid on the full image for grounding. This yields triplets *(full image, crop, question)*. 2. **Two policies, one model** β€” the teacher conditions on the privileged crop, the student on the full image. 3. **On-policy distillation** β€” the student samples a rollout (up to 1024 tokens); at every position both policies are evaluated on the same student prefix, and the per-token JSD between them is minimized (top-K = 100 logits distillation). Gradients flow only through the student. 4. **EMA teacher regularization** β€” the teacher is updated as an exponential moving average of the student (Ξ± = 0.05), which prevents teacher–student co-adaptation and training collapse. ## Quickstart Weights load with standard `transformers` (β‰₯ 5.5): ```python from transformers import AutoModelForImageTextToText, AutoProcessor model_id = "yuanqianhao/Vision-OPD-4B" model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto") processor = AutoProcessor.from_pretrained(model_id) messages = [{ "role": "user", "content": [ {"type": "image", "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}, {"type": "text", "text": "What is the color of the label on the bottle in the background?"}, ], }] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) print(processor.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) ``` Or serve with vLLM (OpenAI-compatible API), as used in our evaluation: ```bash vllm serve yuanqianhao/Vision-OPD-4B --port 8000 ``` To reproduce the reported numbers, use the released evaluation harness: ```bash git clone https://github.com/VisionOPD/Vision-OPD && cd Vision-OPD bash eval/run_eval.sh # see README for API base / judge / benchmark env vars ``` ## Training Details | | | |---|---| | Base model | [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B) (non-thinking mode) | | Data | [Vision-OPD-6K](https://huggingface.co/datasets/yuanqianhao/Vision-OPD-6K) β€” 6.2K synthesized (full image, crop, question) triplets | | Objective | Token-level JSD (Ξ² = 0.5) on on-policy student rollouts, top-K = 100 logits distillation | | Teacher | Same model conditioned on the privileged crop, EMA-regularized (Ξ± = 0.05) | | Rollout length | 1024 tokens, 1 epoch | Training code: [github.com/VisionOPD/Vision-OPD](https://github.com/VisionOPD/Vision-OPD). ## Citation ```bibtex @article{yuan2026vision, title={Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation}, author={Yuan, Qianhao and Lou, Jie and Yu, Xing and Lin, Hongyu and Sun, Le and Han, Xianpei and Lu, Yaojie}, journal={arXiv preprint arXiv:2605.18740}, year={2026} } ``` ## License Released under Apache-2.0, following the base model [`Qwen/Qwen3.5-4B`](https://huggingface.co/Qwen/Qwen3.5-4B).