What is the latest on ORL?

As of Oct 16, on arXiv:2610.20687, the authors say residual context, which carries predictive distributions across denoising steps, is key to the speed-quality trade-off of diffusion language models but has no clear training recipe.

Status: Official — posted on arXiv on Oct 16, 2026 (source: arxiv.org).

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ORL

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Oct 16
Official
Paper introduces ORL, an on-policy method for learning residual context in diffusion language models
Posted on arXiv on Oct 16, 2026 (source: arxiv.org).
On arXiv:2610.20687, the authors say residual context, which carries predictive distributions across denoising steps, is key to the speed-quality trade-off of diffusion language models but has no clear training recipe. They introduce ORL, a framework for on-policy residual learning in DLMs. The paper has not yet been peer-reviewed.
Source: arXiv cs.LG

ORL

Paper introduces ORL, an on-policy method for learning residual context in diffusion language models