# CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making

arXiv: https://arxiv.org/abs/2605.01457 · HTML: https://mlsanity.com/paper/2605.01457/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 50 of its 59 references that are in the map.
- First posted on arXiv 2 May 2026.

## Placed in

- Offline RL and imitation learning from data / [Offline multi-agent RL](https://mlsanity.com/offlinerl/offline-multi-agent-rl/)

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning](https://mlsanity.com/paper/2208.06193/) (2022)
- [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025)
- [One Step Diffusion via Shortcut Models](https://mlsanity.com/paper/2410.12557/) (2024)
- [IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies](https://mlsanity.com/paper/2304.10573/) (2023)
- [Discrete Flow Matching](https://mlsanity.com/paper/2407.15595/) (2024)
- [Diffusion Policy Policy Optimization](https://mlsanity.com/paper/2409.00588/) (2024)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Flow Q-Learning](https://mlsanity.com/paper/2502.02538/) (2025)
- [Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling](https://mlsanity.com/paper/2209.14548/) (2022)
- [FACMAC: Factored Multi-Agent Centralised Policy Gradients](https://mlsanity.com/paper/2003.06709/) (2020)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Flow Matching in Latent Space](https://mlsanity.com/paper/2307.08698/) (2023)
- [Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning](https://mlsanity.com/paper/2304.12824/) (2023)

## Built on by (most cited first, 2 of 2)

- [G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies](https://mlsanity.com/paper/2609.31286/) (2026)
- [MA-FPPO: Multi-Agent Flow-Pretrained Policy Optimization](https://mlsanity.com/paper/2609.32594/) (2026)

## Abstract (arXiv)

How can generative offline multi-agent reinforcement learning achieve both fast joint trajectory generation and effective cooperation? Multi-agent diffusion models generate joint trajectories through iterative sampling but incur high inference latency. Distillation reduces sampling steps but may lose the teacher's cross-agent dependencies, reducing the success rate of cooperative tasks. A joint few-step generator can be trained directly to avoid losing these dependencies during distillation. However, insufficient modeling of cross-agent dependencies may still impair cooperation. In addition, its consistency training process introduces heavy computational and memory costs. Therefore, we propose Coordinated Few-Step Flow (CoFlow), which directly learns a joint averaged-velocity field for distillation-free one-step and few-step multi-agent trajectory generation. To support cooperation during generation, Coordinated Velocity Attention (CVA) incorporates teammate trajectory information into each agent's prediction. To reduce the consistency-training cost of this joint model, we approximate the correction term using finite differences. Across 48 configurations on MPE and SMAC, CoFlow supports centralized and decentralized execution. Under centralized execution, CoFlow outperforms our reproduced baseline by 11.2%, averaging relative gains equally across both benchmark suites, with a 12.93-fold model-sampling speedup. CVA improves normalized scores by 165.1% on average over the same model with cross-agent attention disabled. Compared with the exact-derivative implementation, finite-difference training achieves a 1.78-fold speedup and reduces peak GPU memory by 41.1%. Project page: https://github.com/Guowei-Zou/coflow.