README.md

metadata

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.

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:

hf download Guowei-Zou/CoFlow-checkpoints --local-dir CoFlow-checkpoints

Download one configuration, for example MPE Spread Expert with CoFlow-C:

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:

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

@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},
}