Proximal policy optimization (PPO) is an algorithm that aims to improve the stability of training by avoiding overly large policy updates. It is a popular and effective method used for training [ reinforcement learning]() models in complex environments. To achieve this, PPO uses a ratio that indicates the difference between the current policy and the old policy and clips this ratio within a specific range, ensuring that the policy updates are not too large and the training process is more stable...

Proximal policy optimization

An introduction to Reinforcement Learning (RL), a machine learning method where an agent learns to make decisions by interacting with an environment. This article covers the basics of RL, including how it works, common algorithms, and its application in training models with Large Language Models (LLMs).

Introduction to reinforcement learning and its application with LLMs

A Reward model is a critical component in Reinforcement Learning for Large Language Models (LLMs), designed to evaluate and score the quality of generated responses. It plays a key role in aligning LLMs with human values and improving their output through iterative refinement.

Reward model

An overview of Open Assistant, an open-source chat-based AI assistant, and its implementation of Reinforcement Learning from Human Feedback (RLHF). This article covers the three-step process of RLHF, system requirements, and detailed setup instructions for training the model using Supervised Fine-Tuning, Reward Modeling, and Reinforcement Learning.

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