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We prove that DG-PG reduces policy-gradient estimator variance from $\\mathcal{O}(N)$ to $\\mathcal{O}(1)$, preserves the equilibria of the cooperative game, and achieves agent-independent sample co","title":"Descent-Guided Policy Gradient for Scalable Cooperative Multi-Agent Learning","url":"https://arxiv.org/abs/2602.20078","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.20078v3 Announce Type: replace-cross \nAbstract: Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise. When agents share a common reward, each agent's learning signal is computed from a shared return that depends on all agents, so the stochasticity of the other agents enters the signal as cross-agent noise that grows with $N$. Fortunately, many engineering systems, such as cloud computing and power systems, have differentiable analytical models that prescribe efficient system states, providing a new reference beyond noisy shared returns. 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