While direct policy optimization methods exist, state-ofthe-art LLMs adopt RL-based methods (usually PPO) in RLHF to train the policy to generate good responses guided by a reward model learned from preference data. The main challenge of these methods is the inaccuracy of the intermediate ...
大语言模型-RLHF(六)-PPO(Proximal Policy Optimization)原理&实现&代码逐行注释 Laniakea 4 人赞同了该文章 一,前言 从open AI 的论文可以看到,大语言模型的优化,分下面三个步骤,SFT,RM,PPO,我们跟随大神的步伐,来学习一下这三个步骤和代码实现,本章介绍PPO代码实现。 上章我们介绍了PPO算法的公式,其形式...
This repository contains the code to reproduce experiments in the paper: Policy Optimization in RLHF: The Impact of Out-of-preference Data.The experiments show that policy optimization with out-of-preference data is key to unlocking the reward model's generalization power.How...
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Method description Constrained Generative Policy Optimization was introduced by Meta in a recent paper (https://arxiv.org/pdf/2409.20370). It seems to outperform PPO and DPO and is specifically designed to address standard RLHF limitatio...
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs). Over-optimization occurs when a reward model serves as an imperfect proxy for human ...
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Using these methods, we train a evaluation model with minimal expert-labeled data, which then effectively labels nine times more preference pairs for further RLHF training. For instance, our model using Direct Preference Optimization (DPO) gains around over 1% average improvement on AlpacaEval2, ...
Praveenkumar S, Agyekum EB, Ampah JD, Afrane S, Velkin VI, Mehmood U, Awosusi AA (2022) Techno-economic optimization of PV system for hydrogen production and electric vehicle charging stations under five different climatic conditions in India. Int J Hydrogen Energy 47(90):38087–38105. http...