Reinforcement learning from human feedback (RLHF)

https://www.coursera.org/learn/generative-ai-with-llms/lecture/NY6K0/reinforcement-learning-from-human-feedback-rlhf

Reinforcement learning from human feedback (RLHF) - Week 3 | Coursera

 

In this video, the speaker discusses the use of fine-tuning with human feedback to enhance a language model's ability to generate text summaries. They introduce the concept of Reinforcement Learning from Human Feedback (RLHF), which employs reinforcement learning techniques to align language models (LLMs) with human preferences and ensure they produce useful, relevant, and non-harmful outputs. The video explains RLHF's potential applications, including personalization of LLMs for individual users. It then provides an overview of reinforcement learning concepts, using the example of training a Tic-Tac-Toe-playing agent. The text describes how RLHF adapts these concepts to fine-tune large language models, with the LLM's generated text as actions, and human preferences as rewards. The video discusses methods for determining rewards, including human evaluation and reward models, and explains how the LLM updates its weights iteratively to maximize rewards and align with human preferences.

 

이 비디오에서 스피커는 텍스트 요약 작업을 고려하며 모델이 긴 글에서 가장 중요한 요소를 잡아내기 위해 미세 조정을 사용하여 모델의 능력을 향상시키려고 합니다. 그들은 Reinforcement Learning from Human Feedback (RLHF)의 개념을 소개하며 이 기술을 사용하여 언어 모델 (LLM)을 인간의 선호도와 일치시키고 유용하고 관련성 있고 해로운 출력을 생성하도록 보장합니다. 이 비디오에서는 개인 사용자에게 맞춤화된 LLM과 같은 RLHF의 잠재적인 응용 프로그램을 설명합니다. 그런 다음 틱택토 플레이 에이전트를 훈련하는 예제를 사용하여 강화 학습 개념을 개요로 제공합니다. RLHF가 이러한 개념을 어떻게 대형 언어 모델을 미세 조정하는 데 적응시키는지에 대한 설명으로, LLM의 생성된 텍스트를 동작으로 사용하고 인간의 선호도를 보상으로 사용합니다. 이 비디오에서는 보상을 결정하는 방법에 대한 방법을 논하며, 인간 평가 및 보상 모델을 포함하고, LLM이 보상을 최대화하고 인간의 선호도와 일치하도록 가중치를 반복적으로 업데이트하는 방법을 설명합니다.