TABOM models inference unmasking preferences as a Boltzmann distribution over predictive entropies and derives a ranking loss to align DLM training with observed trajectories, yielding gains in new domains and reduced catastrophic forgetting versus standard SFT.
Taming masked diffusion language models via consistency trajectory reinforcement learning with fewer decoding step
2 Pith papers cite this work. Polarity classification is still indexing.
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CAGenMol uses condition-aware discrete diffusion coupled with reinforcement learning to generate valid molecules meeting multiple heterogeneous constraints, outperforming prior methods on binding affinity, drug-likeness, and success rate benchmarks.
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Self-Distilled Trajectory-Aware Boltzmann Modeling: Bridging the Training-Inference Discrepancy in Diffusion Language Models
TABOM models inference unmasking preferences as a Boltzmann distribution over predictive entropies and derives a ranking loss to align DLM training with observed trajectories, yielding gains in new domains and reduced catastrophic forgetting versus standard SFT.