Unverified50% confidenceFactExact time
Rejection Fine-Tuning (RFT) is a post-training paradigm that requires no human preference annotations, originally popularized by Meta in the LLaMA 2 research.
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10/7/2026
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UnverifiedCurrent mainstream LLMs universally adopt a pre-training plus fine-tuning paradigm, first performing unsupervised pre-training on internet-scale corpora, then aligning with human preferences through techniques such as RLHF.70% similarVerified从预训练检查点到可商用产品需经历监督式指令微调（SFT）和基于人类反馈的强化学习（RLHF）两个后训练阶段69% similarVerifiedThe three-stage alignment training paradigm of SFT–reward model–RLHF was first systematically proposed by OpenAI in the InstructGPT paper.68% similarUnverifiedThe study starts from the same pretrained GPT-2 Small checkpoint and applies two matched conditions separately: standard continued training and adversarial continued training.68% similarUnverifiedMDT uses LoRA (Low-Rank Adaptation) as its primary fine-tuning method to reduce training cost.68% similar
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