Training
- Data: ViRL39K, 38,870 examples
- Rollouts per prompt: 8
- Rollout / actor global batch: 384 / 128
- Maximum prompt / response: 4096 / 256 tokens
- Sampling: temperature 1.0, top-p 0.99
- AdamW bf16: learning rate 1e-6, weight decay 0.01
- NAS-AS drop quantile: 0.25
- AAC target loss ratio: 0.3
- Reference-policy KL: disabled
- Hardware topology: 16 H100-class GPUs; rollout tensor parallelism 8
The paper's main results do not separately report a score for this optimizer/scale combination. No unpublished score is claimed here.
Usage
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
repo = "ANke121/NAS-PO-DAPO-Qwen3-VL-32B"
model = Qwen3VLForConditionalGeneration.from_pretrained(repo, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(repo)
This is a research checkpoint. Outputs may be incorrect; verify them before consequential use. Citation metadata will be added after the paper is public.