Papers

Test-time Multi-agent Coordination by Decomposed Value Gradient Flow

This preprint introduces SCOUT, an offline multi-agent reinforcement learning framework that pairs a per-agent flow-matching behavior prior with a decomposed value function and applies test-time Stein variational refinement to action candidates. Across SMACv1 and MA-MuJoCo, it reports the best average scores among all listed baselines (SMACv1 average reward 16.1 vs 14.9 for MAC-Flow; MA-MuJoCo normalized score 82.12 vs 76.37 for MAC-Flow) and improves on all reported offline-to-online transitions. The result matters because it shifts the coordination/value trade-off from a fixed training-time regularization to an adjustable inference-time compute budget.