TMLR Paper: Rigorous derivation of Adjoint Matching via Stochastic Maximum Principle
rishabh16_ · x · 2026-08-26
A TMLR paper provides a rigorous, first-principles derivation of the Adjoint Matching algorithm from the Stochastic Maximum Principle (SMP), fixing the general case.
The algorithm appears in various applications:
- Fine-tuning diffusion models
- Boltzmann sampling
- Reinforcement Learning for robots
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