How ChatGPT, Claude, and Gemini Are Trained: The 3-Stage Pipeline

Behind every interaction with ChatGPT, Claude, or Gemini lies a sophisticated training methodology that transforms raw computational potential into coherent, helpful AI assistants. Understanding this three-stage pipeline isn't just academic curiosity—it's essential knowledge for anyone working with or building upon modern AI systems, including those developing video generation and synthetic media tools.

Stage 1: Pretraining—Building the Foundation

The first stage of large language model (LLM) training is pretraining, often called the most computationally expensive phase of the entire process. During pretraining, the model learns to predict the next token in a sequence by ingesting massive amounts of text data—we're talking trillions of tokens from books, websites, code repositories, and academic papers.

The objective is deceptively simple: given a sequence of words, predict what comes next. This next-token prediction task, when scaled to billions of parameters and trillions of tokens, produces emergent capabilities that researchers are still working to fully understand. The model learns grammar, facts, reasoning patterns, and even rudimentary coding abilities—all from this single objective.

For models like GPT-4, Claude 3, and Gemini Ultra, pretraining can consume thousands of GPUs running for months, with training compute costs reaching into the hundreds of millions of dollars. This is why the pretraining recipe—the data mixture, the learning rate schedules, the architectural choices—remains one of the most closely guarded secrets at companies like OpenAI, Anthropic, and Google DeepMind.

Stage 2: Supervised Fine-Tuning (SFT)—Teaching Instructions

A pretrained model is impressive but problematic. It's essentially a sophisticated autocomplete system that might continue any prompt in unexpected ways. Ask it to write a poem, and it might instead predict what a web page about poems would look like. This is where supervised fine-tuning (SFT) enters the pipeline.

During SFT, human contractors create high-quality demonstrations of desired behavior. They write examples showing how the model should respond to various prompts: answering questions directly, following instructions precisely, admitting uncertainty, and refusing harmful requests. These demonstrations typically number in the tens of thousands to hundreds of thousands of examples.

The model is then fine-tuned on this demonstration data, learning to associate user queries with helpful, direct responses rather than web-page-style continuations. After SFT, the model behaves much more like an assistant—it understands the conversational format and attempts to be helpful rather than just predictive.

This stage is particularly relevant for synthetic media applications. Models trained on instruction-following become capable of understanding nuanced creative directions—essential for AI systems generating video, audio, or images from text prompts.

Stage 3: RLHF—Alignment Through Human Feedback

The final stage, Reinforcement Learning from Human Feedback (RLHF), is what separates modern chatbots from their predecessors. While SFT teaches the format of good responses, RLHF teaches the model to distinguish between responses of varying quality.

The process works in two phases. First, human raters compare pairs of model outputs and indicate which response is better. These comparisons train a reward model—a separate neural network that learns to predict human preferences. Second, the main model is optimized using reinforcement learning (typically Proximal Policy Optimization, or PPO) to generate responses that score highly according to the reward model.

Anthropic has notably extended this approach with Constitutional AI (CAI), where the model critiques and revises its own outputs according to a set of principles before human feedback is applied. Google's approach with Gemini incorporates similar iterative refinement techniques.

Why This Matters for Synthetic Media

The three-stage pipeline isn't limited to text models. Video generation systems like Sora, Runway Gen-3, and Pika increasingly adopt similar training philosophies. Pretraining on massive video datasets establishes foundational understanding of motion, physics, and visual coherence. Fine-tuning aligns outputs with human creative intent. And preference optimization—whether through RLHF or related techniques like Direct Preference Optimization (DPO)—helps these models produce outputs that humans actually want.

Understanding these training stages also illuminates why deepfake detection remains challenging. Each stage adds layers of learned behavior that make synthetic outputs more convincing, more aligned with human expectations, and harder to distinguish from authentic content.

The Evolving Landscape

While this three-stage approach dominates current practice, the field continues evolving. Techniques like Direct Preference Optimization (DPO) offer alternatives to traditional RLHF that skip the reward modeling step. Some researchers advocate for larger SFT datasets to reduce RLHF dependence. Others explore self-play and constitutional methods that reduce reliance on expensive human labeling.

For practitioners in AI video generation and digital authenticity, these architectural decisions ripple downstream. The training methodology shapes not just capability but also the types of artifacts and patterns that detection systems must identify. As the pipeline evolves, so too must our approaches to verification and authentication.

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