arXiv (All)
open access
CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making
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 los
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Flow3D-OPD: Multi-Teacher On-Policy Distillation for 3D Geometry Generation with Flow-Matching Diffusion Transformer#975194
- MR-STORM: Scalable Multi-Arm Control By Distributed MPC#972414
- Multi-Marginal Flow Matching with Adversarially Learnt Interpolants#799890
- Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech#1131166
- RefAdapt-DiT: Adaptive Joint Attention for Reference-Conditioned Diffusion Transformers#1116746
Record · ID 1049820
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive