Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN)
SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 21, T = 3) trained on a
frozen Qwen/Qwen3.5-4B. This is the initialisation of
the Vision-RL² RL run YuhengSSS/VisionRL2-Qwen3.5-4B.
The backbone weights are unchanged from the base model; only the three attached twig blocks are
trained, from self-distilled attention pseudo-labels (no human RoI annotation).
Configuration
| item |
value |
| backbone |
Qwen/Qwen3.5-4B (frozen) |
| twig |
K = 21, T = 3 |
| stage |
1 (SD-RPN self-distilled pseudo-label training) |
| weights |
full model directory (backbone + twig), bfloat16 safetensors |
| next stage |
region-level RL → YuhengSSS/VisionRL2-Qwen3.5-4B |
How to use
These weights need the modeling code in
YuHengsss/VisionRL2. They are not loadable for
RoI inference through a plain AutoModel / AutoModelForCausalLM call: the RoI gating path
(heatmap head, peak-relative gate, connected-component crop, sub-image splice) lives in the
repository's model classes and evaluation harness.
git clone https://github.com/YuHengsss/VisionRL2
cd VisionRL2
hf download YuhengSSS/SDRPN-Qwen3.5-4B --local-dir output/sdrpn/qwen3_5-4b-sdrpn-K21T3
# stage 2: region-level RL starting from this checkpoint
PHASE_A_CKPT=output/sdrpn/qwen3_5-4b-sdrpn-K21T3 DATASET_ROOT=datasets \
bash scripts/train_rl_qwen3_5_4b.sh
# evaluate the stage-1 checkpoint itself (training-aligned protocol)
MODEL=qwen3_5 CHECKPOINT=output/sdrpn/qwen3_5-4b-sdrpn-K21T3 CAP=576 bash scripts/aligned_eval.sh
Expected numbers (sanity check after download)
Training-aligned protocol (rule metrics, no judge), source limit 576 tokens - the SD-RPN (stage 1)
row of the Vision-RL² README:
| Model |
V* |
ZoomBench |
HR-4K |
HR-8K |
MME-RW Lite |
InfoVQA |
Avg. |
| Qwen3.5-4B (base) |
66.0 |
40.5 |
63.5 |
56.4 |
41.0 |
69.8 |
56.2 |
| SD-RPN (stage 1), this checkpoint |
82.7 |
55.6 |
71.1 |
63.3 |
48.9 |
78.1 |
66.6 |
| Vision-RL² (stage 2) |
85.3 |
61.8 |
77.4 |
70.9 |
51.0 |
80.5 |
71.1 |
Links
- Code: https://github.com/YuHengsss/VisionRL2
- Data (SD-RPN corpora, RL pools, evidence maps): https://huggingface.co/datasets/YuhengSSS/VisionRL2-data
- Collection: https://huggingface.co/collections/YuhengSSS/visionrl2
- Project page: https://yuhengsss.github.io/VisionRL2/
Citation
@article{shi2026visionrl2,
title = {Region-Level Policy Optimization for Fine-grained MLLM Perception},
author = {Shi, Yuheng and Pei, Xiaohuan and Dong, Minjing and Xu, Chang},
journal = {arXiv preprint arXiv:2609.19745},
year = {2026}
}
@inproceedings{shi2026sdrpn,
title = {Catching the Details: Self-Distilled RoI Predictors for Fine-Grained MLLM Perception},
author = {Shi, Yuheng and Pei, Xiaohuan and Dong, Minjing and Xu, Chang},
booktitle = {ICLR},
year = {2026}
}
