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license: mit
library_name: pytorch
tags:
- reinforcement-learning
- multi-agent-reinforcement-learning
- offline-rl
- flow-matching
- generative-models
- pytorch
- arxiv:2605.01457
---
# CoFlow Checkpoints
Official checkpoints for **CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making**.
<p align="center">
<a href="https://arxiv.org/abs/2605.01457"><img src="https://img.shields.io/badge/Paper-arXiv%3A2605.01457-b31b1b?style=for-the-badge&logo=arxiv" alt="Paper"></a>
<a href="https://guowei-zou.github.io/coflow/"><img src="https://img.shields.io/badge/Project-Page-4169e1?style=for-the-badge&logo=githubpages" alt="Project Page"></a>
<a href="https://github.com/Guowei-Zou/coflow-release"><img src="https://img.shields.io/badge/Code-GitHub-111111?style=for-the-badge&logo=github" alt="Code"></a>
<a href="#citation"><img src="https://img.shields.io/badge/Citation-BibTeX-ff8c00?style=for-the-badge" alt="Citation"></a>
</p>
CoFlow is a coordinated few-step generative model for offline multi-agent reinforcement learning. It combines Coordinated Velocity Attention with adaptive coordination gating so multi-agent actions can be generated in one to a few model calls while preserving inter-agent coordination.
## Repository Contents
This repository contains the 120 checkpoints used in the paper:
- 30 task-quality configurations
- 4 model variants per configuration
The task-quality configurations cover:
- MPE: Spread, Tag, and World with `expert`, `medium-replay`, `medium`, and `random` data qualities
- SMAC: `3m`, `8m`, `2s3z`, and `5m_vs_6m` with `Good`, `Medium`, and `Poor` data qualities
- MA-MuJoCo: `2xAnt` and `4xAnt` with `Good`, `Medium`, and `Poor` data qualities
Model variants:
- `coflow-c`: CoFlow with centralized execution
- `coflow-d`: CoFlow with decentralized execution
- `coflow-base-c`: CoFlow-base with centralized execution
- `coflow-base-d`: CoFlow-base with decentralized execution
Each leaf directory contains one paper-used `state_*.pt` checkpoint. See `MANIFEST.tsv` for the mapping from paper configuration to source run, seed, checkpoint step, and file size.
## Download
Download the full checkpoint release:
```bash
hf download Guowei-Zou/CoFlow-checkpoints --local-dir CoFlow-checkpoints
```
Download one configuration, for example MPE Spread Expert with CoFlow-C:
```bash
hf download Guowei-Zou/CoFlow-checkpoints \
--include "mpe/simple_spread/expert/coflow-c/*" \
--local-dir CoFlow-checkpoints
```
## Usage
The checkpoints are intended to be used with the official code release:
```bash
git clone https://github.com/Guowei-Zou/coflow-release.git
```
Please follow the setup, evaluation, and configuration instructions in the GitHub repository. The directory structure in this checkpoint repository is aligned with the paper task names and model variants.
## Citation
```bibtex
@misc{zou2026coflowcoordinatedfewstepflow,
title={CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making},
author={Guowei Zou and Haitao Wang and Beiwen Zhang and Boning Zhang and Hejun Wu},
year={2026},
eprint={2605.01457},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.01457},
}
```
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