Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts

Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before observing the render. As a result, otherwise executable code often yields artifacts with visually salient defects, including overlapping elements, clipped text, broken alignment, low contrast, and overflow. We study visual-feedback self-distillation for code-generated visual artifa

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ethics.ai (9 June 2026), “Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts,” evidence record 1230, https://ethics.ai/record/1230 (originally published by arXiv).

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