AI RESEARCH
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
arXiv CS.LG
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ArXi:2605.16345v1 Announce Type: new Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline paradigms. Online methods, such as RL-based alignment, can directly optimize outcome quality but typically rely on external reward models and iterative rollouts, making them costly and difficult to deploy in many cases.