Datasets:

veggiebird
/

MATPO-data

	---
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	configs:
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	data_files:
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	path: matpo_train_musique/train-*
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	path: matpo_val_frames_repeat_2/train-*
	- config_name: matpo_val_gaia_repeat_8
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	- config_name: single_agent_val_webwalkerqa_repeat_2
	data_files:
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	path: single_agent_val_webwalkerqa_repeat_2/train-*
	license: apache-2.0
	---

	<div align="center">

	# MATPO: Multi-Agent Tool-Integrated Policy Optimization

	Train Multiple Agent Roles Within a Single LLM via Reinforcement Learning.

	<!-- [![arXiv](https://img.shields.io/badge/arXiv-Coming_Soon.svg)](https://arxiv.org/pdf/2510.04678)
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	[![Python 3.10+](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/)
	[![Code](https://img.shields.io/badge/code-GitHub-black.svg)](https://github.com/mzf666/MATPO) -->

	<!-- <hr> -->
	<div align="center">

	[![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)
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	[![Github](https://img.shields.io/badge/Code-000000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/mzf666/MATPO)
	</div>


	</div>

	<div align="center">
	<table>
	<tr>
	<td align="center">
	<img src="assets/main_gaia.png" width="220px" alt="GAIA Results"><br>
	<em>GAIA Results</em>
	</td>
	<td align="center">
	<img src="assets/main_frameqa.png" width="220px" alt="FRAMES Results"><br>
	<em>FRAMES Results</em>
	</td>
	<td align="center">
	<img src="assets/main_webwalkerqa.png" width="220px" alt="WebWalkerQA Results"><br>
	<em>WebWalkerQA Results</em>
	</td>
	</tr>
	</table>
	</div>

	<p align="center">
	<img src="assets/multi_agent_framework.png" width="500px" alt="MATPO Framework">
	</p>


	<p align="center">
	<em>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.</em>
	</p>

	## 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
	```


	<p align="center">
	<img src="assets/single_agent.png" width="600px" alt="Single-agent GRPO Framework">
	<img src="assets/multi_agent_RL_rollout.png" width="600px" alt="MATPO Framework">
	</p>

	<p align="center">
	<em>Comparison between the rollout trajectories between the single-agent GRPO (top) and the multi-agent MATPO (bottom).</em>
	</p>


	### 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


	<p align="center">
	<img src="assets/multi-agent-grpo-implementation.png" width="600px" alt="MATPO Framework">
	</p>

	<p align="center">
	<em>Visualization of MATPO implementation.</em>
	</p>



	## 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

	<details>
	<summary><b>Q: What's the difference between MATPO and traditional multi-agent systems?</b></summary>

	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
	</details>

	<details>
	<summary><b>Q: Can I use MATPO with models other than Qwen3?</b></summary>

	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.
	</details>

	<details>
	<summary><b>Q: How many GPUs do I need for training?</b></summary>

	For Qwen3-14B-base, we recommend:
	- Training: 8x A100/A800 GPUs (80GB)
	- Inference: 1-2x A100/A800 GPUs (40GB/80GB)

	</details>

	<details>
	<summary><b>Q: How does MATPO handle credit assignment?</b></summary>

	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.
	</details>

	<details>
	<summary><b>Q: Can I use MATPO for tasks other than web search?</b></summary>

	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
	</details>

	<details>
	<summary><b>Q: How stable is MATPO training compared to single-agent RL?</b></summary>

	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.
	</details>

	<details>
	<summary><b>Q: Do I need to block HuggingFace URLs during training?</b></summary>

	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.
	</details>

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