GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models

Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likelihood. A dominant and efficient family of methods replaces the likelihood in standard RL with its evidence lower bound (ELBO), estimated from randomly masked sequences. Despite being well aligned with pre-training, these approaches introduce bias through training--inference mismatch by using the ELBO as a likelihood sur

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ethics.ai (28 May 2026), “GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models,” evidence record 3538, https://ethics.ai/record/3538 (originally published by arXiv).

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