Self-Distilled Trajectory-Aware Boltzmann Modeling: Bridging the Training-Inference Discrepancy in Diffusion Language Models

In three linesTABOM, a post-training method for Diffusion Language Models, aligns optimization with the multi-step easy-to-hard decoding trajectory observed at inference. Via Boltzmann modeling of unmasking preferences, it derives a tractable pairwise ranking objective that reduces training-inference discrepancy and improves performance on new domains.

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