DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.
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CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.
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Discrete Flow Matching for Offline-to-Online Reinforcement Learning
DRIFT enables stable offline-to-online fine-tuning of CTMC policies in discrete RL via advantage-weighted discrete flow matching, path-space regularization, and candidate-set approximation.
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Controllable Molecular Generative Foundation Models
CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.