arXiv Machine Learning July 3, 2026 By Carles Domingo-Enrich, Jiequn Han

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control

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arXiv:2604. 08580v2 Announce Type: replace-cross Abstract: Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learning an optimal generative dynamics corresponds to optimizing a control under SDE constraints.

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arXiv Machine Learning

2d ago

Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and It\^o Frameworks

arXiv:2609.38395v1 Announce Type: cross Abstract: Stochastic differential equations (SDEs) can be studied via It\^{o} calculus and rough path theory. For stochastic optimal control, these two framewo...

By Thomas Lew

fine-tuning

arXiv Machine Learning

Jul 15

Continuous Policy and Value Iteration for Stochastic Control Problems and Its Convergence

arXiv:2506. 08121v2 Announce Type: replace-cross Abstract: We introduce a continuous policy-value iteration algorithm where the approximations of the value function of a stochastic control problem and the optimal control are simultaneously updated through Langevin-type dynamics.

By Qi Feng, Gu Wang

arXiv Machine Learning

Sep 1

Data-to-Energy Stochastic Dynamics

arXiv:2509.26364v3 Announce Type: replace Abstract: The Schr\"odinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certa...

By Kirill Tamogashev, Esmeralda S. Whitammer

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jun 16

DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising

arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.

By Paolo Giaretta, Zeyang Li, Navid Azizan

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 11

Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies

arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.

By Zeyang Li, Sunbochen Tang, Navid Azizan

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Sep 24

Value Mirror Descent for Reinforcement Learning

arXiv:2604. 06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL).

By Zhichao Jia, Guanghui Lan

reinforcement-learning

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