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Goal-Conditioned Supervised Learning for LLM Fine-Tuning

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arXiv:2605. 16345v1 Announce Type: new Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment.

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Authors: Shijun Li, Kaiwen Dong, Xiang Gao, Joydeep Ghosh
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Goal-Conditioned Supervised Learning for LLM Fine-Tuning 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, class, rect, under, alignment, eval, line, rate. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.16345

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