Training configuration
- Retained training samples after prompt-length filtering: 6,217
- Maximum prompt length: 5,210 tokens
- Maximum response length: 1,024 tokens
- Training epochs: 1
- Optimizer steps: 64
- Learning rate: 2e-6
- Rollouts per prompt: 8
- Teacher regularization: EMA
- Teacher update rate: 0.05
No benchmark evaluation is included with this checkpoint.
Loading
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "YOUR_ORG/Vision-OPD-Qwen3.5-9B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map="auto",
)
Citation
@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}
}