RACRO (Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization) is a novel framework that enables scalable and modular multimodal reasoning by aligning visual perception with a powerful text-only reasoner. RACRO addresses the key challenge of generating image captions that are both faithful and sufficiently informative for downstream reasoning. It leverages a reasoning-guided reinforcement learning strategy to train the visual extractor, using reward signals derived from the performance of a fixed, high-capacity text-only LLM. This decoupled design avoids costly retraining of vision-language alignments and allows seamless plug-and-play upgrades to more advanced reasoners. Experiments on multimodal math and science benchmarks show that RACRO achieves state-of-the-art performance among open models.

Results

Usage

python

from transformers import AutoProcessor, AutoTokenizer
from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info

########################
# === Configuration ===
########################
IMAGE_PATH = "./assets/images/demo_example.jpg"
QUESTION = "When the canister is momentarily stopped by the spring, by what distance $d$ is the spring compressed?"

MLLM_MODEL_PATH = "KaiChen1998/RACRO-7B-CRO-GRPO"
LLM_MODEL_PATH = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # feel free to use more advanced reasoners!

########################
# === Prompts ===
########################
SYSTEM_PROMPT_CAP = "You are given an image and a relevant question. Based on the query, please describe the image in details. Do not try to answer the question."
SYSTEM_PROMPT_LLM = "You are a helpful assistant."

CAPTION_PROMPT = "Question: {}\nPlease describe the image. DO NOT try to answer the question!"
LLM_PROMPT = """In the following text, you will receive a detailed caption of an image and a relevant question. In addition, you will be provided with a tentative model response. You goal is to answer the question using these information.

### The detailed caption of the provided image: {}

### Note that the caption might contain incorrect solutions, do not be misguided by them.

### A problem to be solved: {}

### A tentative model response: {}

### Note that the above tentative response might be inaccurate (due to calculation errors, incorrect logic/reasoning and so on), under such a case, please ignore it and give your own solutions. However, if you do not have enough evidence to show it is wrong, please output the tentative response."""

########################
# === Initialize Models ===
########################
processor = AutoProcessor.from_pretrained(MLLM_MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_PATH)

mllm = LLM(model=MLLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
           device='cuda:0', dtype="bfloat16", limit_mm_per_prompt={"image": 1})

llm = LLM(model=LLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
          device='cuda:1', dtype="bfloat16")

mllm_sampling = SamplingParams(temperature=0, max_tokens=8192)
llm_sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192)

########################
# === Build Prompts ===
########################
def build_messages(image_path, question):
    cap_msgs = [
        {"role": "system", "content": SYSTEM_PROMPT_CAP},
        {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": CAPTION_PROMPT.format(question)}]}
    ]
    qa_msgs = [
        {"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": question + " Please think step by step. The final answer MUST BE put in \\boxed{}."}]}
    ]
    return cap_msgs, qa_msgs

# === Run Captioning and QA ===
def run_mllm(image_tensor, cap_prompt, qa_prompt):
    cap_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": cap_prompt[0]}], sampling_params=mllm_sampling)
    qa_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": qa_prompt[0]}], sampling_params=mllm_sampling)
    return cap_output[0].outputs[0].text, qa_output[0].outputs[0].text

# === Final Reasoning Step ===
def run_llm_reasoning(caption, question, answer):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT_LLM},
        {"role": "user", "content": LLM_PROMPT.format(caption, question, answer)}
    ]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    output = llm.generate([{"prompt": prompt}], sampling_params=llm_sampling)
    return output[0].outputs[0].text

########################
# === Pipeline ===
########################
cap_msgs, qa_msgs = build_messages(IMAGE_PATH, QUESTION)
cap_prompt = processor.apply_chat_template([cap_msgs], tokenize=False, add_generation_prompt=True)
qa_prompt = processor.apply_chat_template([qa_msgs], tokenize=False, add_generation_prompt=True)

image_tensor, _ = process_vision_info(cap_msgs)
caption_text, tentative_answer = run_mllm(image_tensor, cap_prompt, qa_prompt)
final_answer = run_llm_reasoning(caption_text, QUESTION, tentative_answer)

print("Final Answer:\n", final_answer)

Citation

bibtex

@article{gou2025perceptual,
  author    = {Gou, Yunhao and Chen, Kai and Liu, Zhili and Hong, Lanqing and Jin, Xin and Li, Zhenguo and Kwok, James T. and Zhang, Yu}, 
  title     = {Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning},
  journal   = {arXiv preprint arXiv:2506.04559},
  year      = {2025},
}