EVAN-3B

Built with Qwen. Released under the Qwen Research License, for non-commercial use only.

EVAN-3B is Qwen2.5-VL-3B-Instruct trained with EVAN (EVidence-Ablated Negatives), the method of our NeurIPS 2026 paper Grounding Multimodal Reasoning with Evidence-Ablated Negatives.

Code: github.com/DawnLIU35/EVAN | Models: EVAN-3B · EVAN-7B · EVAN-8B

Method

RLVR rewards typically check only the final answer, not whether it came from the image or from language priors and dataset shortcuts. EVAN masks the visual evidence a question needs, lets the current policy answer without it, and penalizes plausible wrong answers under the clean image. EVAN-3B is trained with DAPO on ViRL39K. The GitHub repository has the full method, data pipeline, and training scripts.

Results

Accuracy (%) from Table 1 of the paper, with sampling at temperature 1.0. DAPO uses the same data and training recipe without EVAN.

Model	Geo3K	MathVista	MathVerse	We-Math	DynaMath	MathVision	MMK12	MMMU-Pro	LogicVista	Avg.
Qwen2.5-VL-3B-Instruct	19.4	46.3	33.5	29.9	40.4	18.2	36.3	19.8	32.1	30.7
DAPO	38.8	62.4	60.4	60.5	59.3	26.5	72.2	29.9	43.0	50.3
EVAN-3B	39.9	62.7	62.8	64.6	60.6	27.6	71.5	31.0	43.3	51.6

Usage

Requires transformers>=4.57, accelerate, and torchvision. Add the instruction below after the question, as in training. The default generation config holds the sampling settings we used for evaluation. With vLLM, also pass top_k=50 in SamplingParams: vLLM skips generation-config values that equal the transformers defaults.

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "DawnLiu/EVAN-3B"
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

question = "Which object is the largest?"
prompt = (
    f"{question}\n\n"
    "You first think through the reasoning process as an internal monologue, enclosed within <think> </think> tags. Then, provide your final answer enclosed within \\boxed{}."
)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/image.png"},
            {"type": "text", "text": prompt},
        ],
    }
]
inputs = processor.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
).to(model.device)
output = model.generate(**inputs)
print(processor.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

EVAN-3B is released under the Qwen Research License of Qwen2.5-VL-3B-Instruct, which permits non-commercial use only. Relative to the base model, the weights were changed by RL training with EVAN, generation_config.json sets our evaluation sampling defaults, and the other configuration, tokenizer, and processor files were re-saved (with dtype set to bfloat16 in config.json).