Training LLMs (Gemini)
The development of Large Language Models (LLMs) has introduced more nuanced learning paradigms, but their core training processes still heavily rely on, or extend from, three fundamental ML learning categories.
LLM computing does not fall neatly into just one category; rather, it typically involves a multi-stage process drawing from all three, with a special emphasis on a fourth category often considered a subset or hybrid: Self-Supervised Learning.
How LLMs typically relate to these categories:
1. Pre-training Phase: Primarily Self-Supervised Learning (A form of Supervised/Unsupervised Hybrid)
Self-Supervised Learning (SSL): This is the dominant method for the pre-training of modern LLMs (like those based on the Transformer architecture).
In SSL, the data itself provides the supervision, without requiring human-labeled examples. The model creates its own "labels" from the input data.
Example: The model is given a sequence of words and its task is to predict the next word (Next-Token Prediction) or to predict a masked-out word (as in BERT).
Although the data is unlabeled by humans, the task of predicting the next token is structured like a supervised learning problem (input = context, output = next word), hence why it's often called self-supervised.
2. Fine-Tuning Phase (Instruction Tuning): Supervised Learning
Supervised Learning: After pre-training, LLMs are often refined using Instruction Fine-Tuning.
This phase uses a dataset of high-quality, human-curated input-output pairs (e.g., an instruction and a desired, well-formed response).
This process explicitly trains the model to follow directions, which is a classic Supervised Learning task (Classification/Regression on the next token prediction).
3. Alignment Phase: Reinforcement Learning
Reinforcement Learning (RL): This category is crucial for aligning the LLM's behavior with human preferences—making it helpful, harmless, and honest.
The most common technique is Reinforcement Learning from Human Feedback (RLHF).
In RLHF:
The LLM generates several responses to a prompt.
Human rankers order these responses from best to worst.
A Reward Model (RM) is trained (often via supervised learning on the human rankings) to score any given response.
The LLM is then fine-tuned using an RL algorithm (like PPO) where the reward signal comes from the RM, effectively training the model (the agent) to produce responses that maximize this human-preference reward within the text generation environment.
The original three-part categorization remains the standard framework for understanding machine learning. LLM computing does not fit into just one bucket but rather leverages Self-Supervised Learning during massive pre-training, is shaped by Supervised Learning during instruction fine-tuning, and is refined for behavior using Reinforcement Learning (RLHF).
Pre-training
This is the most computationally intensive and expensive phase. The goal is to teach the model a general understanding of language, grammar, facts, and reasoning.
Data Collection & Preparation: Massive datasets are collected from a wide variety of sources, including books, articles, websites (like Wikipedia and Common Crawl), and code repositories. This data is then cleaned, filtered for quality and bias, and "tokenized," which means breaking the text down into smaller numerical chunks (tokens) that the model can understand.
The Prediction Task: The model is given a sequence of tokens and trained to predict the next token in the sequence. It's an unsupervised learning task because the "correct" answer is simply the next word in the original text. For example, given the sentence fragment "The quick brown fox jumped over the," the model learns to predict "lazy." It does this repeatedly on trillions of tokens, adjusting its internal parameters to get better and better at predicting the next word.
Supervised Fine-tuning
After pre-training, the model has a broad understanding of language but can be a bit generic. This phase is designed to make the model a more helpful assistant.
Instruction-Response Pairs: The model is trained on a smaller, high-quality dataset of human-written "prompts" and "responses." For example, a human might write the prompt, "What is the capital of France?" and then provide the correct response, "Paris."
Learning to Follow Instructions: This supervised learning step teaches the model to follow specific instructions and produce helpful, human-like responses rather than just continuing a sentence. It helps the model learn to summarize, answer questions, write code, and more.
Reinforcement Learning from Human Feedback (RLHF)
This final phase is crucial for aligning the model's behavior with human values and preferences, making it less likely to generate harmful, biased, or unhelpful content.
Human Ranking: A small team of human reviewers is given a prompt and several different responses generated by the model. They then rank these responses from best to worst. This creates a "reward model."
Optimizing for Human Preference: The reward model learns what humans prefer and gives a "score" to a model's output. The LLM is then fine-tuned again using this reward model. The goal is to encourage the model to generate responses that would receive a high score from the reward model, effectively aligning the model's behavior with the preferences and safety guidelines established by the human reviewers.
While the core, three-phase training process (pre-training, supervised fine-tuning, and reinforcement learning with human feedback) is a common framework used by all the major players like Google, OpenAI, and Meta, the specific techniques and infrastructure they use can differ significantly.
Here's a breakdown of the key similarities and differences:
Core Similarities
Transformer Architecture: All foundational LLMs from these companies are built on the Transformer architecture. This is the groundbreaking neural network design that uses "self-attention" to weigh the importance of different words in a sequence, allowing the model to understand context and relationships in a way that previous models couldn't.
Massive Scale: The scale of training is the single biggest commonality. They all train their models on massive, multi-trillion-token datasets scraped from the public internet, books, and other sources. This requires immense computational resources and dedicated infrastructure.
Three-Stage Training: The general workflow is consistent:
Pre-training: Unsupervised learning on a vast, general text corpus to build a foundational understanding of language.
Fine-tuning: Supervised learning on smaller, curated datasets of human-written prompts and responses to make the model more useful and instruction-following.
RLHF: Using human feedback to align the model's behavior with desired outcomes, making it safer, more helpful, and less prone to generating harmful or biased content.
Key Differences and Specializations
Training Infrastructure: The hardware and software stacks are a major differentiator.
Google uses its own custom-built hardware, called TPUs (Tensor Processing Units), which are specifically designed for machine learning. This gives them a tightly integrated system from the ground up.
OpenAI and Meta primarily use high-end NVIDIA GPUs but have developed their own massive-scale training software and data center designs to optimize performance and reliability. Meta, for instance, has developed its own custom network fabrics (RoCE and InfiniBand) and software to handle the immense data transfer required for training its largest models like Llama.
Training Methodology Refinements: While the high-level process is the same, each company has its own proprietary tweaks and research breakthroughs.
Google has pioneered techniques like multi-modal training from the very beginning, allowing models like Gemini to understand and generate content across text, images, and other data types seamlessly. They also focus on "distillation" to create smaller, more efficient models.
OpenAI has been a key driver in the development and popularization of Reinforcement Learning from Human Feedback (RLHF) as a primary method for aligning model behavior.
Meta has a strong focus on memory-efficient training techniques like Gradient Low-Rank Projection (GaLore), which allows them to pre-train large models on more accessible hardware, such as consumer GPUs, without requiring complex parallelization strategies. This is particularly relevant for their push toward open-source models.
Data and Model Architecture Nuances: The exact composition of the training data and the internal "recipe" for the Transformer architecture (e.g., the number of layers, heads, and other hyper-parameters) are closely guarded secrets. These choices significantly impact a model's final capabilities and characteristics.
In conclusion, the major tech companies all follow the same fundamental playbook for training LLMs.
However, they compete fiercely on the details: the scale of their training data, the efficiency of their hardware and software, and the specific methodological innovations they develop to make their models more capable, reliable, and aligned with their goals.