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

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arXiv:2606. 10334v1 Announce Type: new Abstract: 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.

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Authors: Haoyu Dong
Released: 2026-06-10 (29d ago)
Topic: Related to current project
Relation to my work: Read next because Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, alignment, token, rate, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.10334

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