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A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models
We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC). We consider policy optimization problems and derive
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