AI BriefingKey Stages and Mechanics of RLHF
Key point
This explains the concept of RLHF, which optimizes language models through human feedback, and its three-stage training process.
Details
Defining the quality of text generated by a language model is highly subjective and difficult. Existing metrics such as BLEU or ROUGE are limited to simple rule-based comparisons. To address this, RLHF (Reinforcement Learning from Human Feedback) technology is used to directly optimize models using human feedback.
RLHF plays a key role in aligning models with the complex values of humans (Alignment), and the process is largely divided into three stages.
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Stage 1: Language Model Pretraining This starts from an existing language model trained on a large-scale corpus. The core goal is to secure a model that can respond appropriately to a variety of instructions.
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Stage 2: Reward Model Training This is the stage where human preferences are incorporated into the system. A model is trained to take a text sequence as input and return a scalar reward value that quantifies human preference.
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Stage 3: Fine-tuning via Reinforcement Learning The trained reward model is used to optimize the language model through a reinforcement learning approach.
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