How Anthropic Trained Claude Sonnet and Opus Models: A Deep Dive

Author(s): M

Originally published on Towards AI.

The complete story of Anthropic’s model training journey.

If you’ve ever wondered how Claude learned to be helpful without being harmful, you’re about to find out. This is the story of how Anthropic built one of the world’s safest AI assistants using a technique called Proximal Policy Optimization (PPO) and then went beyond it with their own innovations.

The article delves into the journey of Anthropic, a company founded by former OpenAI researchers, as they developed Claude, an AI assistant. It explores their unique approach to AI alignment, emphasizing the integration of Proximal Policy Optimization (PPO) with Reinforcement Learning from Human Feedback (RLHF). The narrative highlights challenges in balancing helpfulness and harmlessness in AI responses, the innovative use of Constitutional AI for self-improvement, and their collaborative effort to incorporate public input into AI values. The results demonstrated that safety measures can coexist with enhanced capabilities, underscoring the necessity for continuous alignment and thoughtful engineering in future AI developments.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI