Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning 事件

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during training remains critically underexplored. Prevailing fine-tuning paradigms typically treat training prompts as mere surface forms, assuming that semantically equivalent instructions yield identical learning outcomes. However,