Back To Basics Revisiting Reinforce Style Optimization For Learning From Human Feedback In Llms 2024 02 23
Source
cohere.com
Author
Cohere
Date
Context
Training a LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → to follow human preferences may require less machinery than a commonly used method assumes. Cohere's February 2024 research abstract reports that simpler REINFORCE-style reinforcement-learning methods outperformed PPO in the authors' comparisons. PPO requires substantial computation and sensitive adjustment of training settings. The researchers argue that several of its components address problems that matter less in language-model preference training. Their reported result supports testing simpler training methods, with the advantage scoped to the study's comparisons.
Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
RLHF — Reinforcement learning from human feedback — training a model to prefer answers humans rate as better, which turns a raw text predictor into a usable assistant.
alignment — The work of making AI systems actually pursue what their builders and users intend, rather than something subtly or dangerously different.
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