Welcome back to our series on scaling Large Language Models (LLMs)! Following our discussion on how compute trumps human heuristics, let's delve into Reinforcement Learning (RL), where this principle is vividly exemplified. RL is a method of training AI by rewarding desired behaviors and learning from interaction.
1. Success of AlphaGo:
AlphaGo's triumph was primarily due to combining deep neural networks with Monte Carlo tree search (MCTS). It learned from human game records and then through self-play, leading to superhuman Go strategies.
In March 2016, AlphaGo beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap.
2. From AlphaGo to AlphaZero:
AlphaZero represented a major leap, learning entirely through self-play without prior human game data. This approach was applied to not only Go but also chess and shogi (Japanese chess), showcasing a shift from specialized to generalized AI learning systems.
3. From AlphaZero to MuZero:
MuZero extended AlphaZero's capabilities to games with unknown dynamics, learning from environmental interactions without needing game rules. It combined a world model with deep learning and MCTS, advancing towards AI that understands and interacts with complex environments.
4. RL Plays a Central Role in LLM Alignment:
RL is crucial in LLM alignment, particularly in Reinforcement Learning with Human Feedback (RLHF), where LLMs are refined using human feedback for alignment with ethical guidelines.
Looking ahead, RL could significantly enhance LLM training and inference, a topic we'll explore in our next post. RL might guide LLMs towards more accurate, context-aware, and ethically aligned outputs, heralding a new era in AI innovation.