CPPO: Contrastive Perception for Vision Language Policy Optimization

This repository contains the description and implementation of CPPO, a reinforcement learning framework for finetuning vision–language models (VLMs).

🚀 Highlights

• ✨ Contrastive Perception Policy Optimization (CPPO) — A framework for improving vision–language policy reinforcement learning via contrastive perception training.
• 📈 Stronger Empirical Performance — Demonstrates consistent gains on complex multimodal reasoning tasks.
• 🔍 Entropy-Based Perception Token Detection — Automatically locates informative visual tokens through perturbation sensitivity.
• 📊 Contrastive Perception Loss (CPL) — Encourages the policy to gain discriminative perception.
• 🧠 No External Supervision — Perception improvement is gained purely from information-removing and information-preserving augmentations without the use of ground-truth visual information.
• ⚡ Research-Ready Implementation — Includes preprocessing, training, and evaluation pipelines.

Methodology

1. Entropy-Based Perception Token Detection

For each generated response, CPPO identifies perception tokens by measuring the increase in predictive entropy when the input image is replaced with an information-removing perturbation. Tokens with the largest entropy increase are selected as perception-dependent tokens. This process:

• Requires no external supervision

• Is fully model-driven

• Preserves the natural reasoning structure of the VLM

2. Contrastive Perception Loss (CPL)

For each detected perception token, CPPO applies a token-level contrastive loss:

• Anchor: token distribution conditioned on the original image
• Positive: distribution conditioned on an information-preserving perturbation
• Negative: distribution conditioned on an information-removing perturbation

3. Integration with Reinforcement Learning

CPPO augments the standard RL objective with the Contrastive Perception Loss:

• CPL is applied only to perception tokens
• CPL is gated by positive advantage, ensuring it reinforces successful trajectories

This design yields targeted perception improvement while maintaining RL stability.

Main Results

CPPO is evaluated on a wide range of multimodal reasoning benchmarks and consistently improves the baseline RL objective.

Pretrained Models

We provide CPPO-trained checkpoints on HuggingFace.

Model	Training Dataset	HuggingFace Link
CPPO-3B	VIRL-39K	vbdai/CPPO-3B
CPPO-7B	VIRL-39K	vbdai/CPPO-7B

You can load these models using the Hugging Face transformers library:

from PIL import Image
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration

# Load model and processor
model_name = "path/to/cppo-3B"
processor = AutoProcessor.from_pretrained(model_name)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_name, torch_dtype="auto", device_map="auto"
)

# Instruction template
instruction_following = (
    r"You FIRST think about the reasoning process as an internal monologue and then provide the final answer. "
    r"The reasoning process MUST BE enclosed within <think> </think> tags. "
    r"The final answer MUST BE put in \boxed{}."
)

# Prepare prompt with instruction following
prompt = "Your question here. " + instruction_following
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": Image.open("path/to/image.jpg"),
            },
            {"type": "text", "text": prompt},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)

# Generate output
inputs = processor(text=[text], images=image_inputs, padding=True, returvaluationn_tensors="pt").to("cuda")
outputs = model.generate(**inputs,ninstruction to evaluate each checkopoint und with `avg@N` for different benchmarks m
### An Example Workflow
ax_new_tokens=4096)
generated_ids = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, outputs)
]
response = processor.decode(generated_ids[0], skip_special_tokens=True)
print(response)

Training

We provide implementations of CPPO for both synchronous and asynchronous training regimes.
Each variant builds on a widely used large-scale RL framework while adding the CPPO implementation.

Training with CPPO in Synchronous Settings

The synchronous pipeline is built on top of verl, where rollout generation and training are synchronized.

Training with CPPO in Asynchronous Settings

For higher throughput and improved hardware utilization, CPPO is also integrated with AReaL, which decouples generation and training resources.

Quick Start

To train models such as Qwen2.5-VL-3B or Qwen2.5-VL-7B:

1. Navigate to the verl or AReaL directory.
2. Follow the provided environment and dataset setup instructions.
3. Launch training using the CPPO examples.

### For synchronous training
cd verl
bash examples/cppo/run_qwen2_5_vl-3b_virl39k.sh

### For asynchronous training
cd AReaL
bash examples/cppo/run_qwen2_5_vl-3b_geometry3k.sh

Evaluation

For evaluating models on multimodal reasoning benchmarks, we provide an evaluation pipeline based on VLMEvalKit.

Quick Start with Evaluation

Navigate to the evaluation directory and follow the instruction to setup the framework and evaluate each checkpoint (avg@N).

An Example Workflow

cd eval-VLMEvalKit

# Step 1: Run inference
bash run_eval.sh

# Wait for inference to complete...

# Step 2: Find the session name
SESSION_NAME=$(ls -dt ./outputs/Qwen2.5-VL-3B-Instruct/eval_num_g/Qwen2.5-VL-3B-Instruct/T* | head -1 | xargs basename)
echo "Session: $SESSION_NAME"

# Step 3: Post-process group results
python postprocess_group_results.py \
    ./outputs \
    Qwen2.5-VL-3B-Instruct \
    eval_num_g \
    $SESSION_NAME

# Step 4: Generate performance summary
python create_performance_summary.py \
    ./outputs/Qwen2.5-VL-3B-Instruct \
    $SESSION_NAME

# View results
cat ./eval_summary/Qwen2.5-VL-3B-Instruct.csv

Citation

If you find this work useful, please consider giving us a star and citing our work.

@article{rezaei2026cppo,
    title={CPPO: Contrastive Perception for Vision Language Policy Optimization},
    author={Rezaei, Ahmad and Gholami, Mohsen and Ranjbar Alvar, Saeed and Cannons, Kevin and Hossain, Mohammad Asiful and Weimin, Zhou and Zhou, Shunbo and Zhang, Yong and Akbari, Mohammad},
    journal={arXiv preprint arXiv:XXXX.XXXXX},
    year={2026}
}