Discrete Stochastic Localization provides a continuous-state framework with SNR-invariant denoisers on unit-sphere embeddings, enabling one network to support multiple per-token noise paths and improving MAUVE on OpenWebText.
Simplified and generalized masked diffusion for discrete data.Advances in neural information processing systems, 37:103131–103167
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TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.
MemDLM embeds a simulated denoising trajectory into DLM training via bi-level optimization, creating a parametric memory that improves convergence and long-context performance even when the memory is dropped at test time.
DPLM-Evo adds explicit edit operations and a latent alignment space to discrete diffusion protein models, achieving SOTA single-sequence mutation effect prediction on ProteinGym while supporting variable-length generation.
citing papers explorer
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Discrete Stochastic Localization for Non-autoregressive Generation
Discrete Stochastic Localization provides a continuous-state framework with SNR-invariant denoisers on unit-sphere embeddings, enabling one network to support multiple per-token noise paths and improving MAUVE on OpenWebText.
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TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
TAD improves the accuracy-parallelism trade-off in diffusion LLMs via temporal-aware self-distillation that applies hard labels to soon-to-be-decoded tokens and soft supervision to future tokens.
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MemDLM: Memory-Enhanced DLM Training
MemDLM embeds a simulated denoising trajectory into DLM training via bi-level optimization, creating a parametric memory that improves convergence and long-context performance even when the memory is dropped at test time.
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Towards A Generative Protein Evolution Machine with DPLM-Evo
DPLM-Evo adds explicit edit operations and a latent alignment space to discrete diffusion protein models, achieving SOTA single-sequence mutation effect prediction on ProteinGym while supporting variable-length generation.