base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
datasets:
- TIGER-Lab/ViRL39K
license: mit
library_name: transformers
pipeline_tag: video-text-to-text
tags:
- lvlm
- reasoning
- multimodal
- qwen
Spark-VL-7B
⭐ If you find our code or model helpful, please consider giving us a star — your support means a lot! 🏠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
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.