Models

Highlights

• Fully open release of models, training code, evaluation, and the Vero-600K dataset.
• 600K curated RL samples from 59 datasets across 6 visual reasoning categories.
• Trained for broad transfer across chart and OCR, STEM, spatial and action, knowledge and recognition, grounding and counting, and captioning and instruction following.
• SOTA 8B on VeroEval, a 30-benchmark suite for general visual reasoning.
• Improves performance across multiple base model families, including Qwen2.5-VL, Qwen3-VL, and MiMo-VL.

Usage

Example for zlab-princeton/Vero-Qwen35-9B:

python
Copy code
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info

model_path = "zlab-princeton/Vero-Qwen35-9B"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_path)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/image.jpg"},
            {"type": "text", "text": "What is the x axis value with the largest population?"},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=2048)
output = processor.batch_decode(
    generated_ids[:, inputs.input_ids.shape[1]:],
    skip_special_tokens=True,
)[0]
print(output)

Vero models generate a reasoning trace in <think> tags followed by a final answer in <answer> tags. For downstream use, parse the final response from <answer>.

Recommended sampling parameters, following the Qwen3.5 defaults:

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0, max_new_tokens=16384.

Citation

bibtex
Copy code
@misc{sarch2026vero,
  title={Vero: An Open RL Recipe for General Visual Reasoning},
  author={Sarch, Gabriel and Cai, Linrong and Wang, Qunzhong and Wu, Haoyang and Chen, Danqi and Liu, Zhuang},
  year={2026}
}

License

Vero is released under the Apache-2.0 license. Users should also review the licenses and usage terms of the underlying base models and any upstream datasets.