G2MAF: Test-Time Gradient Guidance for Multi-Agent Flow Policies

arXiv:2609.31286 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/g2maf/G2MAF

Project page: https://g2maf.github.io

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Reinforcement Learning — 20× in this paper · explained in 4 episodes
• Action Space — 6× in this paper · explained in 1 episode
• Q-learning — 6× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Actor — 5× in this paper · explained in 3 episodes
• Decode — 4× in this paper · explained in 2 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• In International Conference on Machine Learning — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Coordinated Velocity Attention — 2× in this paper
• Multi-Agent Reinforcement Learning (MARL) — 2× in this paper · explained in 4 episodes
• Action Decoder — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Dec-POMDP — 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
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
• Test-time Offline Reinforcement Learning on Goal-related Experience
• One-Step Flow Policy Mirror Descent
• Scaling Offline RL via Efficient and Expressive Shortcut Models
• Diffusion Guidance Is a Controllable Policy Improvement Operator
• OM2P: Offline Multi-Agent Mean-Flow Policy
• Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning
• Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition
• Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
• CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection