LPDP adds a local re-solving operator to edit-flow DNA generators so that reward signals can guide insertions, deletions, and substitutions without retraining.
International Conference on Learning Representations , year =
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Coupling Models enable single-step discrete sequence generation via learned couplings to Gaussian latents and outperform prior one-step baselines on text perplexity, biological FBD, and image FID metrics.
citing papers explorer
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LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
LPDP adds a local re-solving operator to edit-flow DNA generators so that reward signals can guide insertions, deletions, and substitutions without retraining.
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Coupling Models for One-Step Discrete Generation
Coupling Models enable single-step discrete sequence generation via learned couplings to Gaussian latents and outperform prior one-step baselines on text perplexity, biological FBD, and image FID metrics.