arXiv:2606.18195v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs) remains unexplored. Existing OPSD methods are inherently autoregressive-centric. They inject privileged information via left-to-right prefix conditioning with token-level divergence supervision, a design that fundamentally conflicts with the arbitraryorder generation of dLLMs. We introduce d-OPSD, the first OPSD framework tailored for dLLMs. Our approach makes two core contributions. First, we refra
The development of diffusion LLMs (dLLMs) is a relatively new frontier, and this research addresses a fundamental methodological gap in improving their post-training efficiency, which becomes crucial as these models scale.
Improving post-training for dLLMs with on-policy self-distillation (OPSD) can significantly enhance their performance and cost-efficiency, accelerating their practical adoption in various applications for sophisticated readers.
The introduction of d-OPSD specifically for dLLMs fundamentally changes how these models can be further refined, potentially closing the performance gap with traditional autoregressive LLMs for certain tasks.
- · AI researchers and developers
- · Companies utilizing dLLMs
- · Sectors benefiting from advanced generative AI
- · Inefficient dLLM training methods
dLLMs can be fine-tuned more effectively and cost-efficiently using d-OPSD.
Improved dLLM performance could lead to their wider adoption in creative AI, content generation, and specialized tasks where arbitrary-order generation is beneficial.
The enhanced capability and efficiency of dLLMs might foster new application areas for generative AI, potentially impacting broader sectors like media production and design.
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