Proximal Policy Optimization (PPO) - How to train Large Language Models
Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!
This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.
Full Playlist: • RLHF for training Language Models
Video 0 (Optional): Introduction to deep reinforcement learning • A friendly introduction to deep reinf...
Video 1 (This one): Proximal Policy Optimization
Video 2: Reinforcement Learning with Human Feedback • Reinforcement Learning with Human Fee...
Video 3 (Coming soon!): Deterministic Policy Optimization
00:00 Introduction
01:25 Gridworld
03:10 States and Action
04:01 Values
07:30 Policy
09:39 Neural Networks
16:14 Training the value neural network (Gain)
22:50 Training the policy neural network (Surrogate Objective Function)
33:38 Clipping the surrogate objective function
36:49 Summary
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