Large Language Models (LLMs) have transformed AI and are becoming increasingly ubiquitous, powering chatbots, text summarisation tools, and data extraction pipelines. 

We see models like GPT-4 and LLaMA-3 generate human-like text and comprehend vast amounts of information with apparent ease. But behind that lies a demanding and technically complex training pipeline. 

Understanding how LLMs are trained is key to unlocking their potential, but also avoiding their pitfalls. Training determines not only a model’s capabilities but also its limitations, biases, and performance across different tasks. Whether you’re a developer prompting a domain-specific model or a researcher evaluating model reliability, knowing how LLMs are trained helps you make better decisions.

In this article, we’ll walk you through how LLMs are trained covering the three core phases: initial pre-training, instruction tuning, and model alignment. Along the way, we’ll highlight both the power and the limitations of these models, as well as the technical challenges of building them. 

If you have a basic understanding of machine learning, you’ll be able to follow it all without a problem, and if you’d like to go deeper into how LLMs are trained, and how to use them in practice, join our next cohort of AI & ML Engineers.

1. Initial Pre-Training

The first stage of training a Large Language Model begins with the initial pre-training process. which involves several crucial steps. 

First, we need to carefully collect the data and clean it to prepare it for the task at hand. Next, the raw text goes through tokenization: a process that converts it into a machine-readable format (we’ll get into what tokens are in just a moment.) With tokenized data in place, the model is trained to predict parts of the input based on context from the surrounding text. Finally, we examine how the model’s architecture is optimized and efficiently distributed across modern computing infrastructures, ensuring scalability and performance.

1.1 Data Collection & Preprocessing

Training Modern LLMs typically begins with collecting datasets spanning hundreds of terabytes of raw data. These datasets include diverse sources like Common Crawl, web scrapes, books, academic papers, and code repositories. To gather this volume of information, specialized distributed crawling systems parse billions of HTML pages. 

Figure 1. Web scraping. Source: https://inkbotdesign.com/web-scraping/ 

The raw extracted text then requires cleaning, such as text duplication removal to avoid overfitting, or filtering irrelevant noise and non-textual elements in the process.

To do so, instead of comparing words directly, state‑of‑the‑art pipelines (aka modern systems) embed each document into a high‑dimensional space using pre-trained sentences or document‑level encoders. This makes it easier to compare documents based on what they say, not just the words they use.

For example, the cosine similarity can be computed between two embeddings to represent how close or similar they are to each other.  Based on that similarity score, a threshold can be defined to decide when two documents are considered a duplicate and need to be dropped.

Typically, this approximate deduplication  leverages techniques like locality sensitive hashing (LSH) or efficient nearest neighbor search to scale to billions of items. These methods help identify similar items without needing to compare every possible pair, something that would be too slow at scale. Tools like FAISS, developed by Meta, are commonly used for this purpose because they can handle billions of items quickly.

After text duplication removal, classifiers identify and remove low‑quality text. Modern systems combine rule‑based heuristics (e.g., minimal word count, minimum average word length, or low punctuation density) with neural network models that predict a “readability” or “naturalness” score. These filters are often trained on human‑annotated data and calibrated using perplexity metrics for evaluating large language models. In addition, toxic content detectors and spam classifiers are used to purge content that might degrade model behaviour.

A recent breakthrough is the incorporation of synthetic data into the pre‑training corpus. Techniques similar to the DeepSeek‑R1 “cold‑start” method use reinforcement learning (RL) to generate verified, chain‑of‑thought (CoT) samples in domains such as mathematics and logical reasoning. Here, a model (often a precursor or an expert model) generates candidate reasoning traces that are then validated using rule‑based scoring, such as the result for math problems or the execution for programming tasks. Rejection sampling is applied to keep only high‑quality examples. This synthetic augmentation is critical when high‑value tasks require precise reasoning but annotated data is scarce.

Figure 2. DeepSeek’s cold start approach. The cold start helps skip the unstable phase of RL training. Source: https://nikhilanand03.substack.com/p/why-i-think-deepseek-r1-just-revealed

1.2 Tokenization

Tokenization is the natural next step, consisting of the process of converting raw text into discrete tokens—numerical representations that serve as input to a model. For state‑of‑the‑art LLMs, tokenization must be efficient, robust to out‑of‑vocabulary words, and capable of preserving semantic nuances. Modern systems achieve these goals through subword tokenization methods.

Byte-Pair Encoding (BPE) is one of the most common tokenization methods and is the base of models like GPT-2, GPT-3 and BERT. BPE begins with a vocabulary of individual characters and iteratively merges the most frequent adjacent pairs to form longer tokens. In each iteration, given a corpus of tokens T, the algorithm computes the frequency F(x, y) for each adjacent pair (x, y). The pair with the maximum frequency is merged into a new token z=concat(x,y). This is repeated until the vocabulary reaches a target size. 

This approach not only compresses text but also yields a vocabulary that efficiently represents both common and rare words. To ensure that every possible input string can be represented, regardless of language or unusual characters, many modern LLMs (e.g., GPT‑2 and GPT‑3) use byte-level BPE. This method first converts text into its UTF‑8 byte representation and then applies BPE on these bytes. The resulting vocabulary is typically in the range of 50k–100k tokens, offering a universal solution that avoids out‑of‑vocabulary issues.

Figure 3. Tokenization. Source: https://cognitiveclass.ai/courses/llm-foundations-get-started-with-tokenization

1.3 Training the Model

After preparing high-quality, tokenized data, the next phase is to train a transformer-based language model using self‑supervised objectives. Transformers are a type of deep learning architecture built to understand relationships between words in a sentence, no matter how far apart they are. You can think of a transformer like an attentive reader that looks at the entire sentence before deciding which words matter most for understanding the meaning.

In modern pipelines, training a transformer-based language  model  involves several key components that ensure efficiency, stability, and scalability.

Most large language models are pre-trained using an autoregressive objective. Given a sequence of tokens, the model learns to predict the probability of the next token conditioned on the previous tokens. The objective is to minimize a cross‑entropy loss: this tells the model how far off its predictions are from the correct answers, and encourages it to become more confident when it's right, and less confident when it's wrong.

Figure 4. Next token prediction tasks. The LLMs are initially trained to predict the most likely word in a sentence.

1.4 Computational Optimization

Modern LLM training leverages mixed-precision arithmetic (using FP16 or BF16) to reduce memory consumption and accelerate computations without compromising model accuracy. Techniques such as gradient checkpointing further help in managing the memory footprint during backpropagation, enabling the training of models with hundreds of billions of parameters.

Additionally, training an LLM is computationally intensive, often requiring thousands of GPUs. To tackle this, several distributed training paradigms are combined:

• Data Parallelism: Splitting large batches across multiple GPUs so that each GPU processes a subset of the data and gradients are synchronized after each forward–backward pass.

• Model Parallelism: Partitioning the model itself (e.g., dividing layers or even within layers) across GPUs. Frameworks like Megatron leverage tensor and pipeline parallelism to manage extremely large models.

• Zero Redundancy Optimizer (ZeRO): Used in DeepSpeed, ZeRO partitions optimizer states, gradients, and even parameters across GPUs, dramatically reducing memory usage and enabling the training of trillion-parameter models 

These techniques can be orchestrated by libraries such as DeepSpeed from Microsoft and Megatron from NVIDIA, which provide efficient communication primitives to minimize latency and maximize throughput. For example, DeepSpeed’s ZeRO and pipeline parallelism have been instrumental in training massive models like Megatron-Turing NLG 530B.

2. Instruction Tuning (Supervised Fine-Tuning)

Pre‑training teaches a model the statistical patterns of language, but it does not guarantee that the model responds in a controlled or task‑specific manner. Instruction tuning is the crucial next step that ensures that a model answers prompts in the desired format and tone.

Instruction tuning transforms a general-purpose text generator into a model that understands and obeys detailed instructions. For example, a pre‑trained model might generate plausible continuations of a text prompt, due to its autoregressive nature, but without instruction tuning it might not follow a specified response style or formatting guideline. In a chatbot application, this means the model might produce verbose or off‑topic replies rather than clear, concise answers. By fine‑tuning on curated instruction–response pairs, the model learns the “right way” of answering queries, much like a well‑trained customer support agent.

In this second section, we cover how to curate text datasets for instructions, and how to make the LLM learn from that dataset using supervised fine-tuning.

2.1 Dataset Curation for Instructions

The backbone of effective instruction tuning is a high‑quality dataset where human instructions are paired with ideal responses. Models such as OpenAI’s GPT‑4‑Turbo have been fine‑tuned using data compiled and refined by expert annotators. These experts not only generate high‑quality responses but also enforce consistency in tone, style, and correctness.

Just like in pre‑training, instruction data must be meticulously cleaned and deduplicated. This involves filtering out inconsistent or low‑quality pairs and ensuring that the dataset represents a balanced mix of tasks, from factual question answering to creative tasks, so that the model generalizes well to unseen instructions.

2.2 Supervised Fine-Tuning

Instruction tuning is implemented using a supervised learning setup on top of the pre‑trained model. The goal is to minimize a cross‑entropy loss that compares the model’s output with the curated ideal response. This supervised loss encourages the model to generate outputs that closely match the responses provided by expert annotators. 

Additional techniques, such as incorporating special tokens or format markers, can be used to explicitly signal the structure of the desired output. 

3. Model Alignment

Even after training on massive, high‑quality data and optimizing for next‑token prediction, LLMs can produce outputs that are inaccurate, unsafe, or simply misaligned with human values. Model alignment bridges that gap by ensuring that the model’s behavior reflects the ethical, factual, and usability standards expected in real‑world applications. In this final section we cover some of the state-of-the-art techniques that are currently used to achieve effective model alignment.

3.1 Supervised Fine-Tuning on Curated Instruction Data

The first step in aligning a model is to fine-tune it on a dataset composed of prompt–response pairs that have been rigorously curated by human experts. These datasets are designed to:

• Imbue specific behavior: For example, ensuring that the model responds helpfully, politely, and factually.

• Reduce harmful outputs: By including examples that demonstrate safe and ethical responses.

This stage can significantly reshape a model’s outputs by updating its weights via fine-tuning, based on human-approved examples.

3.2 Reinforcement Learning from Human Feedback (RLHF)

One of the most widely adopted methods for model alignment is Reinforcement Learning from Human Feedback (RLHF). In RLHF:

1. Human evaluators rate model outputs: For a given prompt, multiple responses are generated, and humans rank or score them.

2. Reward models are trained: These scores are used to train a reward model that predicts the quality and safety of an output.

3. Policy optimization: The base model is further trained using an RL algorithm (often a variant of Proximal Policy Optimization) that updates its behavior to maximize the reward signal. This step drives the model toward producing responses that align with human judgments.

Recent work has shown that RLHF can transform a model’s reasoning capabilities. For example, OpenAI’s o1 model (codenamed “Strawberry”) uses RLHF to encourage multi-step, chain-of-thought reasoning, yielding outputs that are significantly more coherent and accurate on challenging tasks.

3.3 Direct Preference Optimization (DPO)

Direct Preference Optimization (DPO) is an emerging method for aligning large language models with human preferences. Instead of using a separate reward model and the complex, sometimes unstable, reinforcement learning loop found in RLHF (Reinforcement Learning from Human Feedback), DPO directly adjusts the model’s probability distribution so that responses humans prefer become more likely.

In simple terms, while RLHF requires human evaluators to rate multiple outputs and then uses these ratings to guide the model through reinforcement learning, DPO streamlines the process. It modifies the training objective itself to “nudge” the model toward generating outputs that align with human judgment—making the training process both more straightforward and more stable.

This approach is useful because it reduces the complexity and potential instability associated with RLHF, while still achieving high-quality, safe, and useful responses. Essentially, DPO offers a more efficient and consistent pathway for aligning model behavior with what people expect and need.

Conclusions

LLMs were a breakthrough in AI, built with state‑of‑the‑art techniques, from massive data collection and sophisticated tokenization, through highly optimized and distributed training methods, to advanced alignment strategies like RLHF, DPO, and chain‑of‑thought prompting. These models are incredibly powerful tools for applications such as chatbots, translation, content creation, and even specialized reasoning tasks in math and science. They excel when used in settings that demand understanding vast amounts of data and multi-step reasoning.

However, despite these advances, LLMs come with significant risks and limitations. Even after careful alignment, these are some areas with ongoing discussions:

• Bias and Inequity: Models may still perform inequitably across different languages, or cultural contexts . Their performance often reflects biases in the underlying training data, meaning they might be less accurate or fair when handling underrepresented languages or demographics.

• Safety Concerns: The potential for harmful outputs or misuse remains, especially if the alignment process does not fully mitigate risks associated with generating dangerous or inappropriate content.

• Reliability and Transparency: The “chain-of-thought” reasoning process can sometimes lead to inconsistent answers or self-contradictions, challenging users’ trust in the system.

• Environmental concerns: Training such AI models can be quite expensive in terms of carbon emissions. Understanding the LLM training pipeline is a fundamental first step to optimise such processes and have more environmentally-responsible models.

• Economic and Societal Considerations: The cost of training and maintaining these models is enormous, and the long-term societal impact, ranging from job displacement to privacy issues, remains an active area of debate.