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Spark-VL-7B

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🌈 Try our demo on 🤗Huggingface Demo

🏠Github repository 📖Daily Paper 🤗models 📖Paper

Introduction

We propose SPARK, a unified framework that integrates policy and reward into a single model for joint and synchronous training. SPARK can automatically derive reward and reflection data from verifiable reward, enabling self-learning and self-evolution. Furthermore, we instantiate this framework on multiple backbones, training SPARK-VL-7B, SPARK-7B, and SPARK-VL-32B. This repo is the SPARK-VL-7B.

📢 News

• 🚀 [09/29/2025] We release our 🤗datasets.
• 🚀 [09/29/2025] We release our Spark's 📖Paper.
• 🚀 [09/29/2025] We upload our evaluation code and 🤗models.
• 🚀 [09/29/2025] We release Spark 🏠Github repository.

💡 Highlights

• 🔥 Synergistic Policy–Reward Co-Evolving (SPARK): We introduce SPARK, a unified reinforcement fine-tuning framework that jointly optimizes policy and reward within a single model through on-policy co-evolution..
• 🔥 Recycling Rollouts: Unlike conventional RL pipelines that discard rollouts after policy updates, SPARK recycles RLVR rollouts into pointwise, pairwise, and reflection objectives, enabling the model itself to act as both a strong policy and a generative reward model.
• 🔥 Co-Evolving Mechanism: Improved reward accuracy provides better gradients for policy learning, while stronger reasoning further refines reward judgment, forming a positive feedback loop that enhances reasoning, judgment, and reflection in synergy.
• 🔥 Efficient and Practical: SPARK requires no human preference data, teacher models, or external reward models, making it significantly more data- and compute-efficient than traditional RM-based RL pipelines.

🛠️ Usage

🤗 Using Transformers

Our model is based on Qwen2.5-VL-7B-Instruct. You can use the same code as the Qwen2.5-VL-7B-Instruct model for inference, referring to 🤗Huggingface.

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
 "internlm/Spark-VL-7B",
 torch_dtype=torch.bfloat16,
 attn_implementation="flash_attention_2",
 device_map="auto",
)

processor = AutoProcessor.from_pretrained("internlm/Spark-VL-7B")

messages = [
 {
 "role": "user",
 "content": [
 {
 "type": "image",
 "image": image_path,
 },
 {"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)
inputs = processor(
 text=[text],
 images=image_inputs,
 videos=video_inputs,
 padding=True,
 return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
 out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
 generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

🔦 Using vLLM

We recommend using vLLM for faster inference speed. Using vLLM leads to significant speed improvements in dataset evaluation.

PORT=8019
N_PROC=256
SERVE_NAME=spark_vl_7b
MODEL_PATH=/internlm/Spark-VL-7B

CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve "$MODEL_PATH" \
 --tensor-parallel-size 4 \
 --served-model-name $SERVE_NAME \
 --port $PORT \
 --max-num-seqs $N_PROC

Training

Spark Training

After downloading the dataset, you can start training using the following example bash script. Our bash scripts are in /Spark/Lmm_XC/XC/scripts/spark_training You need to modify the dataset paths and model paths to your own locations.

export WORKSPACE_DIR="/fs-computility/....../Lmm_XC" # Path to project root directory
export DATASET_PATH="/fs-computility/....../infer_data_ViRL_19k.json" # Path to your dataset
export PRETRAIN_MODEL_PATH="/fs-computility/....../Qwen2.5-VL-7B-Instruct" # Path to pretrained model
export WANDB_PROJECT="Observation" # Name for this project
export MODEL_CPK_NAME="Qwen2.5-VL-7B-GRPO-virl-19k-iar-reflection-hyb-diverse-bs64-e2" # Name for this training run
export LOG_PATH='/fs-computility/....../Qwen2.5-VL-7B-GRPO-virl-19k-iar-reflection-hyb-diverse-bs64-e2.txt' #Log file save path


export WANDB_API_KEY="......"
export SAVE_PATH="/fs-computility/....../${WANDB_PROJECT}/${MODEL_CPK_NAME}" # Absolute path to save everything about this training run
export CKPT_PATH="${SAVE_PATH}/ckpt" # Path to save checkpoints 
export FINAL_CKPT_PATH="${SAVE_PATH}/final_ckpt" # Path to save final checkpoints
export TIMESTAMP=$(date +%Y%m%d_%H%M%S) # Timestamp
export CUR_LOG_DIR="${SAVE_PATH}/training_logs/${TIMESTAMP}" # Path to save current run logs
export LOG_DIR="${SAVE_PATH}/tb_logs" 

⏰ Attention:

export DEV_MODE=0 # Set to 1 for debug mode on single dev machine

Evaluation

The integrated multimodal mathematics dataset can be downloaded from 🤗datasets and evaluated using the scripts provided in the Evaluation folder. The evaluation results will be stored, and accuracy can subsequently be computed with the calculate_acc.py file.

bash ./Evaluation/eval_spark_vl_7b.sh
python calculate_acc.py --result_path ./your_result_path.json

✒️Citation

@article{liu2025spark,
 title={SPARK: Synergistic Policy And Reward Co-Evolving Framework},
 author={Ziyu Liu and Yuhang Zang and Shengyuan Ding and Yuhang Cao and Xiaoyi Dong and Haodong Duan and Dahua Lin and Jiaqi Wang},
 journal={arXiv preprint arXiv:2509.22624},
 year={2025}
}

📄 License

👁 Code License
👁 Data License
Usage and License Notices: The data and code are intended and licensed for research use only. License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use

Acknowledgement

We sincerely thank projects lmm-r1 and OpenRLHF for providing their open-source resources.

Base model

Qwen/Qwen2.5-VL-7B-Instruct

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