Logit-KL Flow Matching recovers the flow-matching velocity field from conditional likelihood maximization and uses iterative denoise-re-noise sampling to improve perplexity and downstream metrics over prior NAR baselines on text and code tasks.
Weinberger, and Yoav Artzi
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Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Logit-KL Flow Matching recovers the flow-matching velocity field from conditional likelihood maximization and uses iterative denoise-re-noise sampling to improve perplexity and downstream metrics over prior NAR baselines on text and code tasks.