GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
arXiv:2605.29398v1 Announce Type: new Abstract: 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 surrogate, which can degrade performance. In this work,
The paper addresses a current technical challenge in applying reinforcement learning effectively to large language models, specifically the 'training-inference mismatch' in diffusion-based architectures.
Improving reinforcement learning techniques for diffusion LLMs can lead to more robust and performant AI models, accelerating progress in generative AI capabilities.
This research proposes a new method, GDSD, to overcome biases in reinforcement learning for diffusion LLMs, potentially leading to better alignment and performance of these models.
- · AI researchers
- · Developers of generative AI models
- · Cloud computing providers
- · Users of large language models
- · AI methods with significant training-inference mismatch
- · Less efficient reinforcement learning approaches
More accurate and efficient training of diffusion language models through Guided Denoiser Self-Distillation (GDSD).
Accelerated development of advanced AI agents capable of higher-level reasoning and interaction.
Increased competition and innovation in the AI agent sector, potentially leading to new applications across industries.
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