Training
- Data: ViRL39K, 38,870 multimodal reasoning 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
- Optimizer: AdamW bf16, learning rate 1e-6, weight decay 0.01
- Reference-policy KL: disabled
- NAS-AS: drop quantile 0.25, minimum scale 0.9, square-root excess
- AAC: JSD over q/rho, target loss ratio 0.3, coefficient range [0, 10]
- Hardware: 8 NVIDIA H100 80 GB GPUs
Evaluation
The paper reports 68.04 overall mean Accuracy@8 over eight configurations from seven out-of-domain multimodal reasoning benchmarks. Accuracy@8 is mean response accuracy, not pass@8.
Use the public NAS-PO evaluator with 8 samples, temperature 1.0, top-p 0.99, and 256 new tokens.
Usage
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model = Qwen3VLForConditionalGeneration.from_pretrained(
"ANke121/NAS-PO-GRPO-Qwen3-VL-8B",
torch_dtype="auto",
device_map="auto",
)
processor = AutoProcessor.from_pretrained("ANke121/NAS-PO-GRPO-Qwen3-VL-8B")
This is a research checkpoint. Outputs may be incorrect; verify them before consequential use. Citation metadata will be added after the paper is public.