Multi-Agent Coordination via Support-Preserving Distillation

arXiv:2610.10087 · cs.LG, cs.MA, cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.LG, cs.MA, cs.RO

Submitted: 2026-10-07

Updated: 2026-10-08

Project page: https://alex6095.github.io/mosdot/Abstract

Terminology

• Actor — 49× in this paper · explained in 3 episodes
• Distillation — 44× in this paper · explained in 2 episodes
• Benchmark — 26× in this paper · explained in 2 episodes
• Flow Matching — 20× in this paper · explained in 8 episodes
• Reinforcement Learning — 17× in this paper · explained in 4 episodes
• Aggregation — 13× in this paper · explained in 1 episode
• Consistency — 12× in this paper · explained in 3 episodes
• Optimal Transport — 10× in this paper · explained in 5 episodes
• Shared-Randomness Variant — 9× in this paper
• AUC — 6× in this paper · explained in 2 episodes
• Action Space — 6× in this paper · explained in 1 episode
• Regularization — 5× in this paper · explained in 3 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Q-learning — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Entanglement — 2× in this paper · explained in 4 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Dec-POMDP — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Function — 1× in this paper · explained in 2 episodes

Sources

• Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning
• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
• Generative Modeling via Drifting
• Value-Decomposition Networks For Cooperative Multi-Agent Learning
• The StarCraft Multi-Agent Challenge
• Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

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