New research frames Adjoint Matching via Stochastic Maximum Principle

A new research paper, "Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control," by Jiequn Han, rigorously derives and generalizes the Adjoint Matching method for stochastic optimal control problems. The work formulates a Hamiltonian adjoint matching objective and demonstrates its connection to the Hamilton-Jacobi-Bellman stationarity conditions. For cases with state- and control-independent diffusion, the paper recovers a previously introduced lean adjoint matching loss, while highlighting the necessity of additional terms when diffusion is state-dependent. The research offers a practical, implementable alternative to traditional Stochastic Maximum Principle algorithms, particularly in stochastic settings where martingale terms pose challenges. AI

IMPACT Provides a new framework for optimizing generative models and sampling techniques.

RANK_REASON Academic paper on a novel control method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Adjoint Matching
• Hamiltonian adjoint matching
• Hamilton--Jacobi--Bellman
• Jiequn Han
• optimal control
• stochastic control
• Stochastic maximum principle for partially observed risk‐sensitive optimal control problems of mean‐field forward‐backward stochastic differential equations

• paper
• other

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