--- dataset_info: - config_name: matpo_train_musique features: - name: dataset dtype: string - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: evidence_list list: 'null' - name: index dtype: int64 - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: question_type dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 29724892 num_examples: 6175 download_size: 1088403 dataset_size: 29724892 - config_name: matpo_val_frames_repeat_2 features: - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: index dtype: string - name: metadata struct: - name: level dtype: int64 - name: reasoning_types dtype: string - name: row_number dtype: int64 - name: source dtype: string - name: wiki_links list: string - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: search_and_browse struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 8706034 num_examples: 1648 download_size: 408571 dataset_size: 8706034 - config_name: matpo_val_gaia_repeat_8 features: - name: dataset dtype: string - name: level dtype: int64 - name: task_id dtype: string - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: evidence_list list: 'null' - name: index dtype: int64 - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: question_type dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 4360455 num_examples: 824 download_size: 72077 dataset_size: 4360455 - config_name: matpo_val_webwalkerqa_repeat_2 features: - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: index dtype: string - name: metadata struct: - name: difficulty_level dtype: string - name: domain dtype: string - name: golden_path list: string - name: lang dtype: string - name: source_website list: string - name: type dtype: string - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: search_and_browse struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 7471252 num_examples: 1360 download_size: 452476 dataset_size: 7471252 - config_name: single_agent_train_musique features: - name: dataset dtype: string - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: evidence_list list: 'null' - name: index dtype: int64 - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: question_type dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 36449467 num_examples: 6175 download_size: 1220027 dataset_size: 36449467 - config_name: single_agent_val_frames_repeat_2 features: - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: index dtype: string - name: metadata struct: - name: level dtype: int64 - name: reasoning_types dtype: string - name: row_number dtype: int64 - name: source dtype: string - name: wiki_links list: string - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 10451730 num_examples: 1648 download_size: 384560 dataset_size: 10451730 - config_name: single_agent_val_gaia_repeat_8 features: - name: dataset dtype: string - name: level dtype: int64 - name: task_id dtype: string - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: evidence_list list: 'null' - name: index dtype: int64 - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: question_type dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 5257791 num_examples: 824 download_size: 72587 dataset_size: 5257791 - config_name: single_agent_val_webwalkerqa_repeat_2 features: - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: answer dtype: string - name: index dtype: string - name: metadata struct: - name: difficulty_level dtype: string - name: domain dtype: string - name: golden_path list: string - name: lang dtype: string - name: source_website list: string - name: type dtype: string - name: need_tools_kwargs dtype: bool - name: question dtype: string - name: split dtype: string - name: tools_kwargs struct: - name: google_search struct: - name: create_kwargs struct: - name: ground_truth dtype: string - name: scrape struct: - name: create_kwargs struct: - name: ground_truth dtype: string splits: - name: train num_bytes: 8858574 num_examples: 1360 download_size: 412503 dataset_size: 8858574 configs: - config_name: matpo_train_musique data_files: - split: train path: matpo_train_musique/train-* - config_name: matpo_val_frames_repeat_2 data_files: - split: train path: matpo_val_frames_repeat_2/train-* - config_name: matpo_val_gaia_repeat_8 data_files: - split: train path: matpo_val_gaia_repeat_8/train-* - config_name: matpo_val_webwalkerqa_repeat_2 data_files: - split: train path: matpo_val_webwalkerqa_repeat_2/train-* - config_name: single_agent_train_musique data_files: - split: train path: single_agent_train_musique/train-* - config_name: single_agent_val_frames_repeat_2 data_files: - split: train path: single_agent_val_frames_repeat_2/train-* - config_name: single_agent_val_gaia_repeat_8 data_files: - split: train path: single_agent_val_gaia_repeat_8/train-* - config_name: single_agent_val_webwalkerqa_repeat_2 data_files: - split: train path: single_agent_val_webwalkerqa_repeat_2/train-* license: apache-2.0 ---
# MATPO: Multi-Agent Tool-Integrated Policy Optimization Train Multiple Agent Roles Within a Single LLM via Reinforcement Learning.
[![Models](https://img.shields.io/badge/Models-5EDDD2?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/veggiebird/MATPO-14b) [![Data](https://img.shields.io/badge/Data-0040A1?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https://huggingface.co/datasets/veggiebird/MATPO-data) [![Paper](https://img.shields.io/badge/Paper-000000?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.04678) [![Github](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/mzf666/MATPO)
GAIA Results
GAIA Results 		FRAMES Results
FRAMES Results 		WebWalkerQA Results
WebWalkerQA Results

MATPO Framework

MATPO allows planner and worker agents to coexist within a single LLM and be trained via RL, achieving an 18.38% relative improvement over single-agent baselines on GAIA-text, FRAMES, and WebWalker-QA.

## News & Updates - **[2025-Oct-08]** MATPO-Qwen3-14B checkpoints and rollouts released - **[2025-Oct-08]** Code and training scripts released - **[2025-Oct-06]** Arxiv Paper released ## Overview **MATPO** (Multi-Agent Tool-Integrated Policy Optimization) is a novel reinforcement learning framework that enables training multiple specialized agent roles (planner and worker agents) within a single large language model. ### The Problem Current single-agent approaches for multi-turn tool-integrated planning face critical limitations: - **Context Length Bottleneck**: Tool responses (e.g., web scraping) consume excessive tokens, making long-range planning prohibitive - **Noisy Tool Responses**: Raw tool responses interfere with the model's attention and planning capabilities ### Our Solution MATPO introduces a **multi-agent-in-one-model** architecture where: - A **planner-agent** orchestrates high-level planning and delegates subtasks - **Worker-agents** handle specific browsing and search tasks with isolated contexts - Both roles are trained within a **single LLM** using role-specific prompts via reinforcement learning ## Key Features - **Multi-Agent-in-One-Model**: Train planner and worker agents within a single LLM using role-specific system prompts - **Principled Credit Assignment**: Extends GRPO with theoretically grounded reward distribution across planner and worker rollouts - **Easy Integration**: Built on top of [veRL](https://github.com/volcengine/verl), compatible with existing RL training frameworks - **Robust Training**: More stable learning curves compared to single-agent approaches, especially with noisy tool responses - **Infrastructure Efficient**: No need for deployment of separate models or additional rollout engines ## MATPO Architecture MATPO employs a hierarchical multi-agent framework where a single LLM serves multiple roles: ``` User Query → Planner Agent → Subtask 1 → Worker Agent → Result 1 → Subtask 2 → Worker Agent → Result 2 → ... → Final Answer ```

Single-agent GRPO Framework MATPO Framework

Comparison between the rollout trajectories between the single-agent GRPO (top) and the multi-agent MATPO (bottom).

### Multi-Agent Rollout Process 1. **Planner Agent**: - Receives user query with planner-specific system prompt - Generates high-level plan and decomposes it into subtasks - Delegates subtasks to worker agents - Synthesizes worker responses into final answer 2. **Worker Agent**: - Receives subtask with worker-specific system prompt - Performs multi-turn tool-integrated planning (search, scrape, analyze) - Returns summarized result to planner - Maintains isolated context to prevent token overflow 3. **Credit Assignment**: - Final answer accuracy determines the reward - Reward is normalized across all planner-worker rollout groups - Gradient flows to both planner actions and worker actions proportionally

MATPO Framework

Visualization of MATPO implementation.

## Quick Start Prerequisites: - Python 3.10 or higher - CUDA 12.4+ (for GPU support) - 16 x (8 x 80G-A800) GPUs (for training with Qwen3-14B-base) Clone the repository. ```bash git clone https://github.com/mzf666/MATPO.git cd MATPO ``` For prerequisites installation (CUDA, cuDNN, Apex), we recommend following the [verl prerequisites guide](https://verl.readthedocs.io/en/latest/start/install.html#pre-requisites) which provides detailed instructions for: - CUDA: Version >= 12.4 - cuDNN: Version >= 9.8.0 - Apex Setup environment and install dependencies. ```bash conda create -n matpo python==3.10 -y conda activate matpo bash examples/sglang_multiturn/install.sh ``` Setup Node.js for Serper API support. MCP (Model Context Protocol) requires Node.js to run MCP servers. Node.js version 18+ is recommended for optimal compatibility with MCP tools. ```bash target_path=YOUR_TARGET_PATH # Download Node.js binary (example for Linux x64) wget https://nodejs.org/dist/v24.2.0/node-v24.2.0-linux-x64.tar.xz # Extract to your target path tar -xf node-v24.2.0-linux-x64.tar.xz -C $target_path # Add to PATH export NODEJS_HOME=$target_path/node-v24.2.0-linux-x64 export PATH=$NODEJS_HOME/bin:$PATH export NODE_SHARED=$target_path/node-shared/node_modules export PATH=$NODE_SHARED/.bin:$PATH # Verify installation node --version npm --version # Install serper mcp server mkdir -p $target_path/node-shared cd $target_path/node-shared npm init -y npm install serper-search-scrape-mcp-server ``` Configure the Node.js paths and HTTP / HTTPS proxies (if necessary) in the `examples/sglang_multiturn/launch.sh` script properly. Download the training and testing datasets to the `data` directory. The prerpocessed datasets can be downloaded [here](https://huggingface.co/datasets/veggiebird/MATPO-data). Train a Qwen3-14B-base model with MATPO on the MuSiQue dataset and evaluate on the GAIA-text datasets: ```bash # tested on 16 x (8 x 80G-A800) nodes export SERPER_API_KEY="YOUR_SERPER_API_KEY" && \ export OPENAI_API_KEY="YOUR_OPENAI_API_KEY" && \ export WANDB_API_KEY="YOUR_WANDB_API_KEY" && \ export SINGLENODE=true && \ export RAY_DEBUG=legacy && \ export HYDRA_FULL_ERROR=1 && \ source YOUR_CONDA_PATH activate matpo && \ cd YOUR_PROJECT_PATH && \ bash examples/sglang_multiturn/launch.sh \ examples/sglang_multiturn/qwen3-14b_musique_MATPO.sh ``` ## Experiments and Results ### Main Results MATPO consistently outperforms single-agent GRPO baselines across all benchmarks: | Method | GAIA-text | WebWalkerQA | FRAMES | Relative Average Improvement | |--------|-----------|-------------|---------|---------------------| | Single-Agent GRPO | 32.16% | 30.14% | 56.22% | - | | **MATPO (Ours)** | **42.60%** | **33.00%** | **63.64%** | **+18.38%** | ### Training Configuration - **Base Model**: Qwen3-14B-base - **Training Dataset**: Filtered MuSiQue dataset. - **Training Steps**: 180 steps - **Rollouts per Query**: 8 (for group normalization) - **Reward Function**: 0.9 × accuracy + 0.1 × tool_format_reward ### Model Checkpoints and Rollouts We release the trained Qwen3-14B-base model checkpoints at the 180th training step of both [single-agent GRPO](https://huggingface.co/veggiebird/MATPO-single-agent-14b) and [MATPO](https://huggingface.co/veggiebird/MATPO-14b). The associated model rollouts across various training steps can be found [here](https://huggingface.co/datasets/veggiebird/MATPO-rollout). ### Key Findings - **More Stable Training**: MATPO exhibits more stable learning curves and avoids catastrophic performance drops observed in single-agent training - **Robustness to Noise**: Multi-agent decomposition effectively isolates noisy tool responses, preventing them from interfering with high-level planning - **Better Credit Assignment**: Principled reward distribution across planner and worker rollouts leads to more effective learning ### Practical Implementation Tips Based on our experiments, we recommend: - **Final Summary**: Final summaries from worker agents are critical for clean planner-worker interfaces - **Query Recap**: Recapping original user query in worker prompt significantly improves performance - **URL Blocking**: Remember to blocking HuggingFace search results to avoid data leakage ## Citation If you find MATPO helpful in your research, please consider citing our paper: ```bibtex @misc{mo2025multiagenttoolintegratedpolicyoptimization, title={Multi-Agent Tool-Integrated Policy Optimization}, author={Zhanfeng Mo and Xingxuan Li and Yuntao Chen and Lidong Bing}, year={2025}, eprint={2510.04678}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.04678}, } ``` ## Acknowledgments We would like to thank: - **VolcEngine** for developing and open-sourcing [veRL](https://github.com/volcengine/verl), the RL training framework that powers MATPO - **Alibaba Cloud** for the Qwen3 model series - **Google** for the Serper API that enables web search capabilities - The authors of **GAIA**, **WebWalkerQA**, **FRAMES**, and **MuSiQue** datasets - The open-source community for valuable feedback and contributions ## FAQ
Q: What's the difference between MATPO and traditional multi-agent systems? MATPO uses a single LLM to play multiple agent roles via different system prompts, rather than deploying separate models. This offers: - Lower infrastructure complexity - Better parameter efficiency - Easier deployment and maintenance - Compatible with existing RL frameworks
Q: Can I use MATPO with models other than Qwen3? Yes! MATPO is model-agnostic. You can use any decoder-only LLM that supports tool calling and multi-turn conversations. We've tested with Qwen3-14B-base, but models like Llama 3, Mistral, or other reasoning-capable LLMs should work.
Q: How many GPUs do I need for training? For Qwen3-14B-base, we recommend: - **Training**: 8x A100/A800 GPUs (80GB) - **Inference**: 1-2x A100/A800 GPUs (40GB/80GB)
Q: How does MATPO handle credit assignment? MATPO extends GRPO with principled credit assignment: 1. The planner's final answer determines the accuracy reward 2. This reward is normalized across all rollouts in a group 3. Gradients flow proportionally to both planner and worker actions 4. Worker agents receive the same advantage value as their parent planner rollout See our paper for more details.
Q: Can I use MATPO for tasks other than web search? Absolutely! While our paper focuses on web search, MATPO's framework is general. You can extend it to: - Code generation with execution feedback - Scientific reasoning with calculator tools - Data analysis with pandas/SQL tools - Any multi-turn task with verifiable rewards
Q: How stable is MATPO training compared to single-agent RL? MATPO is significantly more stable. Our experiments show: - Single-agent GRPO often suffers catastrophic drops after step 120 - MATPO maintains steady improvement throughout training - Multi-agent structure isolates noisy tool responses, preventing interference See Figure 4 in our paper for training curves.
Q: Do I need to block HuggingFace URLs during training? For research integrity, yes - especially if your evaluation benchmarks are hosted on HuggingFace. This prevents models from "cheating" by finding ground-truth answers online. For production systems with no data leakage concerns, this is optional.
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