PersonaVLM: Long-Term Personalized Multimodal LLMs

Official implementation of PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization.

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📋 Table of Contents

• 🚀 News
• 🌟 Overview
• 📊 Persona-MME Benchmark
• 🚀 Performance
• 🚀 Quick Start
• 💻 Interactive Gradio Demo
• ✒️ Citation

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🚀 News

• [2026.04] 📢 PersonaVLM officially Open-Sourced! Model weights, training data, and the Persona-MME benchmark are now released!

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🌟 Overview

PersonaVLM transforms general-purpose MLLMs (e.g., Qwen2.5-VL) into personalized assistants capable of long-term interaction. It achieves this through a collaborative two-stage process featuring three core capabilities:

• 🧠 Remembering: Proactively extracts and summarizes multimodal conversational histories into a structured, multi-type database (Core, Semantic, Episodic, and Procedural memories).
• 💡 Reasoning: Conducts multi-turn reasoning by dynamically retrieving and integrating relevant long-term memories based on the conversation context.
• 🤝 Response Alignment: Infers the user's latest latent traits using a Momentum-based Personality Evolving Mechanism (PEM), ensuring generated responses are deeply aligned with the user's evolving characteristics.

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📊 Persona-MME Benchmark

     

To rigorously evaluate long-term personalization in multimodal settings, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases designed to assess MLLMs across 14 fine-grained personalization tasks.

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🚀 Performance

Extensive experiments demonstrate that PersonaVLM significantly enhances a model's personalization capabilities and consistently outperforms strong counterparts, including proprietary models like GPT-4o and leading open-source alternatives. Under a 128k context setting, PersonaVLM achieves substantial improvements of 22.4% on Persona-MME and 9.8% on PERSONAMEM, surpassing GPT-4o by 5.2% and 2.0%, respectively.

🔍 Click to view Qualitative Examples

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🚀 Quick Start

1. Repository Structure

Before starting, ensure your project directory is organized as follows. The core reasoning and memory management logic is encapsulated within the PersonaVLM/ module.

PersonaVLM/
├── checkpoints/
│   ├── rl/                              # Download PersonaVLM weights here
│   ├── openai/clip-vit-base-patch32     # Local CLIP model
│   └── sentence-transformers/all-MiniLM-L6-v2 
├── data/
│   ├── Persona-MME/                         # Benchmark dataset for evaluation
│   └── training/                        # SFT and RL synthesized datasets
├── PersonaVLM/                               # Core PersonaVLM Agent Logic
│   ├── model.py
│   ├── PersonaVLMAgent.py
│   ├── prompts.py
│   ├── retriever.py
│   ├── tools.py
│   └── utils.py
├── train/                               # SFT and RL training scripts
├── eval.py                              # Evaluation script for Persona-MME
├── inference.py                         # CLI inference script
└── gradio_demo.py                       # Interactive Web UI

2. Environment Setup

Create a conda environment and install the required dependencies.

conda create -n PersonaVLM python=3.10 -y
conda activate PersonaVLM
pip install -r requirements.txt

[!WARNING]

Since PersonaVLM is built upon Qwen2.5-VL, it is strictly required to install transformers==4.51.3 to prevent compatibility issues.

3. Prepare Models and Data

• Model Weights: Download the official PersonaVLM Model and place it under ./checkpoints/rl.
• Retrieval Models: Download CLIP and all-MiniLM-L6-v2 models to the ./checkpoints/ directory.
• Datasets: Download Persona-MME (for evaluation) and PersonaVLM-Dataset (for training) into ./data/.

4. Inference

We provide a CLI script to chat with the PersonaVLM agent. It supports both standard reasoning and forced retrieval configurations:

python inference.py 
Optional arguments:
--force-retrieve   : Force the agent to execute memory retrieval for every message.
--reasoning-mode   : Flag to toggle reasoning (default is True; passing this sets it to False).

5. Evaluation (Persona-MME)

To evaluate the model's long-term personalization capabilities on the Persona-MME benchmark:

python eval.py \
    --model_path ./checkpoints/rl \
    --bench_context 32k \
    --savedir ./output/32kPersonaMMEresults

6. Training Pipeline

PersonaVLM is trained using a two-stage pipeline: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

Stage 1: Supervised Fine-Tuning (SFT)

Note on Qwen2.5-VL: The Qwen2.5-VL repository used for training does not support mixing pure-text and multimodal samples in a single batch. To resolve this, we append a dummy conversation to each text-only sample and mask the final interaction step during loss computation (Implementation details in ./train/sft/Qwen2.5-VL/qwen-vl-finetune/qwenvl/data/data_qwen.py).

# 1. Regenerate data with dummy convs
python ./data/training/sft/regenerate.py
2. Launch SFT training
cd ./train/sft/Qwen2.5-VL/qwen-vl-finetune
CUDAVISIBLEDEVICES=0,1,2,3,4,5,6,7 sh scripts/sft_PersonaVLM.sh

Stage 2: Reinforcement Learning (GRPO)

Our RL stage is implemented based on the ms-swift framework. Key modifications for our custom reward design are located in scripts/qwen_server.py and examples/train/grpo/plugin/plugin.py.

Before starting RL, you must deploy the reward model (we use Qwen3-30B-A3B-Instruct-2507) via vLLM:

# 1. Deploy the Reward Model Server
MASTERPORT=29501 CUDAVISIBLEDEVICES=6,7 python -m vllm.entrypoints.openai.apiserver \
    --model /path/to/Qwen3-30B-A3B-Instruct-2507 \
    --tensor-parallel-size 2 \
    --host 0.0.0.0 \
    --port 8080 \
    --trust-remote-code \
    --gpu-memory-utilization 0.9
(Optional) Test the reward server connection
python ./train/ms-swift-main/scripts/qwen_server.py
2. Launch RL Training
cd ./train/ms-swift-main
CUDAVISIBLEDEVICES=0,1,2,3,4,5 sh scripts/rl.sh

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💻 Interactive Gradio Demo

We provide an interactive Gradio-based playground to explore the "R3" process (Proactive Remembering, Multi-step Reasoning, and Personality-based Response Alignment). The UI allows you to visualize the agent's internal cognitive steps.

Launch Locally

To start the demo, ensure your model weights are in ./checkpoints/rl and run:

python gradio_demo.py

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✒️ Citation

If you find our work helpful, please cite: ```latex @inproceedings{nie2026personavlm, title={PersonaVLM: Long-Term Personalized Multimodal LLMs}, author={Nie, Chang and Fu, Chaoyou and Zhang, Yifan and Yang, Haihua and Shan, Caifeng}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2026}, url={http://arxiv.org/abs/2604.13074} }