ANke121/NAS-PO-DAPO-Qwen3-VL-8B
ANke121/NAS-PO-DAPO-Qwen3-VL-8B is an 8 billion parameter Qwen3-VL-8B-Instruct model fine-tuned using DAPO and Native Attention-Strategy Policy Optimization (NAS-PO) for vision-language tasks. This model incorporates trajectory-level NAS-AS advantage scaling and positive-anchored token-level AAC routing consolidation. It is designed for vision-language applications, achieving a reported 67.94 overall mean Accuracy@8 across various benchmarks.
Loading preview...
Model Overview
ANke121/NAS-PO-DAPO-Qwen3-VL-8B is an 8 billion parameter vision-language model based on the Qwen3-VL-8B-Instruct architecture. It has been specifically fine-tuned using a novel optimization approach combining DAPO (Deep Advantage Policy Optimization) with NAS-PO: Native Attention-Strategy Policy Optimization for Vision-Language Models.
Key Differentiators
- NAS-PO Integration: Enhances the DAPO optimization backbone by adding trajectory-level NAS-AS advantage scaling and positive-anchored token-level AAC routing consolidation.
- Specialized Training: Trained on the ViRL39K dataset, comprising 38,870 examples, with specific rollout and batch configurations.
- Performance: The model reports a 67.94 overall mean Accuracy@8 across eight configurations from seven benchmarks, indicating its capability in vision-language understanding.
Training Details
- Data: ViRL39K (38,870 examples)
- Optimization: AdamW bf16 with a learning rate of 1e-6 and weight decay of 0.01.
- Hardware: Utilized 8 NVIDIA H100 80 GB GPUs.
Usage Considerations
This model is presented as a research checkpoint. Users should verify outputs before using them in consequential applications. Citation metadata will be provided upon public release of the associated paper.