Pre‑training vs Fine‑tuning: How AI Learns Its Basics Hosted by Nathan Rigoni

In this episode we break down the two foundational stages that turn raw data into useful AI systems: pre‑training and fine‑tuning. What does a model actually learn when it reads billions of words or scans millions of images, and how do we reshape that knowledge into behaviors like answering questions, writing code, or describing pictures? By the end you’ll see why the split between “learning the mechanics” and “learning the behavior” is a game‑changer for building adaptable, efficient models— and you’ll be left wondering: could the next wave of AI rely more on clever fine‑tuning than on ever‑larger pre‑training datasets?

What you will learn

• The goal of pre‑training: teaching a model the fundamental mechanics of language or vision through massive supervised (next‑token) learning.
• How fine‑tuning shifts focus to behavior, using methods such as RLHF, instruction tuning, code‑tuning, and agentic fine‑tuning.
• A concrete case study: ServiceNow’s Apriel model, which starts from the multimodal Pixtral backbone and is fine‑tuned for conversational VLM capabilities.
• Trade‑offs in data volume, compute cost, and model size when choosing between larger pre‑trained models and smaller models enhanced by aggressive fine‑tuning.
• Key terminology you’ll hear repeatedly: supervised pre‑training, reinforcement learning from human feedback (RLHF), instruction tuning, RLVR, multimodal fine‑tuning.

Resources mentioned

• OpenAI “GPT‑4 Technical Report” (details on pre‑training and RLHF).
• ServiceNow blog post on Apriel and the underlying Pixtral model.
• Christiano et al., “Fine‑Tuning Language Models from Human Preferences” (2017).
• Hugging Face’s guide to instruction fine‑tuning with Transformers.
• Papers on multimodal vision‑language models such as CLIP and Flamingo.

Why this episode matters
Grasping the distinction between pre‑training and fine‑tuning lets engineers, product leaders, and AI enthusiasts make smarter choices about model selection, cost management, and deployment strategy. Whether you’re building a chatbot, an image captioner, or an autonomous agent, knowing when to invest in massive pre‑training versus targeted fine‑tuning can dramatically impact performance, latency, and scalability. This insight also highlights a growing trend: leveraging modest multimodal backbones and unlocking them with specialized fine‑tuning to create edge‑friendly AI solutions.

Subscribe for more bite‑sized AI deep dives, visit www.phronesis-analytics.com, or email [email protected]. Your feedback shapes future episodes—keep the conversation going!

Keywords: pre‑training, fine‑tuning, RLHF, instruction tuning, multimodal models, vision‑language models, Pixtral, Apriel, reinforcement learning from human feedback, AI curriculum learning, model behavior, edge AI.