Summary
This part explored how large language models are trained and deployed, from foundational pre-training through alignment techniques to recent developments.
We studied pre-training and fine-tuning. Pre-training on massive text corpora teaches models general language patterns, requiring enormous computational resources. Fine-tuning adapts models to specific tasks using smaller, targeted datasets.
We examined instruction tuning and RLHF. Instruction tuning trains models on examples of following instructions. A common RLHF pipeline uses human comparisons to train a reward model, then optimizes responses against that model. These methods can improve assistant behavior, but preferred answers are not necessarily correct answers.
We traced the rise of GPT and publicly available models from early developments through GPT-3’s 175 billion parameters to ChatGPT’s mainstream breakthrough (late 2022). The landscape includes proprietary models and open-weight alternatives, including releases from Meta and Mistral. Open weights and open source are different: the license determines the rights and restrictions that apply.
Finally, we examined recent trends: reasoning models using extended inference-time computation (OpenAI o1, DeepSeek-R1), efficiency techniques like Mixture of Experts, multimodal capabilities integrating text, images, and audio, and systems integration through RAG and tool use.
Key Takeaways
- Pre-training on massive corpora creates versatile base models learning general language patterns.
- Fine-tuning adapts models to specific tasks using smaller datasets.
- Instruction tuning and RLHF can improve instruction following and alignment with human preferences, without guaranteeing accuracy.
- GPT evolution progressed from research tools to mainstream applications, with ChatGPT marking widespread adoption.
- Open-weight models have broadened access; their licenses vary, and not all are open source.
- Recent trends include reasoning models, efficiency techniques, multimodal capabilities, and systems integration.