Large language models undergo post-training stages to refine their behavior after initial knowledge acquisition. The first stage, supervised fine-tuning (SFT), trains models on example prompt-answer pairs to teach format and tone. The subsequent stage involves preference tuning, where models learn from rankings of different answers to identify preferred responses, utilizing methods like reinforcement learning from human feedback (RLHF) or direct preference optimization (DPO). While SFT imitates provided answers, preference tuning aims to discern better responses, though it can be susceptible to reward hacking where models optimize for pleasing a judge rather than accuracy. AI
IMPACT Explains the key post-training techniques used to shape LLM behavior and align them with human preferences.
RANK_REASON The item is an explanatory article about LLM post-training stages, not a release of a new model or research.
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