Summary
Highlights
This section introduces the foundational stage of building LLMs like ChatGPT: pre-training. It begins with the crucial step of downloading and processing vast amounts of internet data. The FineWeb dataset, collected and curated by Hugging Face, is presented as a representative example, highlighting the need for a huge quantity and diversity of high-quality documents. The process starts with Common Crawl, which has indexed billions of web pages since 2007. This raw data undergoes several filtering stages: URL filtering to remove undesirable content (malware, spam, adult sites), text extraction to convert raw HTML into plain text, language filtering (e.g., keeping only English content above a certain percentage), and removal of personally identifiable information (PII). This rigorous process results in a massive, yet manageable, dataset (e.g., 44 terabytes for FineWeb, equivalent to 15 trillion tokens) ready for the next steps in model development.
Before text can be fed into neural networks, it must be converted into a one-dimensional sequence of finite symbols, known as tokens. Initially, text can be represented as raw bits (0s and 1s), but this results in extremely long sequences. To reduce length while increasing symbol diversity, bits are grouped into bytes (256 possible symbols). Further compression and efficiency are achieved using the Byte Pair Encoding (BPE) algorithm, which identifies and merges frequently occurring pairs of symbols into new, unique symbols. This iterative process expands the vocabulary size while shortening the overall sequence length. For example, GPT-4 uses over 100,000 possible symbols (tokens). The Ticktokenizer website is demonstrated as a tool to visualize how text is tokenized, revealing that even small changes in wording or spacing can result in different token sequences. This tokenized sequence, often trillions of tokens long, becomes the input for neural network training.
This section delves into the core of LLM development: neural network training. The objective is to model the statistical relationships between tokens. This is achieved by feeding 'windows' of tokens (sequences up to a maximum length, e.g., 8,000 tokens) into a neural network. The neural network's task is to predict the next token in the sequence. With a vocabulary of over 100,000 possible tokens, the network outputs a probability distribution over all possible next tokens. Initially, these probabilities are random due to the network's random initialization. The training process involves iteratively updating the network's parameters (weights) based on the difference between its predictions and the actual next token in the training data. This 'nudging' ensures that the correct token's probability increases, making the model's predictions consistent with the statistical patterns observed in the massive text dataset. This process occurs in parallel across large batches of tokens, constituting the computationally intensive phase of LLM development.
The internal workings of neural networks are explored, emphasizing that they are complex mathematical functions with billions of parameters. These parameters can be thought of as 'knobs' that are adjusted during training to align the network's output with observed data patterns. The Transformer architecture, a common choice for LLMs, is briefly introduced, highlighting its layered structure of operations like token embeddings, attention blocks, and multi-layer perceptrons. Crucially, these 'neurons' are described as simple, stateless computational units, distinct from biological neurons. Following training, the model enters the inference stage, where it generates new data. This involves providing a prefix of tokens to the trained network, which then predicts the next token based on its learned probability distribution. This process is iterative, with each newly generated token being appended to the sequence to predict the subsequent one. Because of the stochastic nature of sampling from probability distributions, the generated sequences are 'remixes' and not exact copies of the training data, allowing for creative and varied outputs.
The video uses OpenAI's GPT-2 (published in 2019) as a concrete example, calling it the first 'recognizably modern stack.' GPT-2 had 1.6 billion parameters, a maximum context length of 1,024 tokens, and was trained on approximately 100 billion tokens—numbers considered small by today's standards. Replicating GPT-2's training in 2024 is significantly cheaper ($600 compared to $40,000 in 2019), attributed to improved data quality, faster hardware (GPUs), and optimized software. The live demonstration of training a GPT-2 model illustrates the iterative process of updating parameters, observing the 'loss' decrease (indicating improved prediction accuracy), and periodically generating text to assess coherence. This continuous cycle highlights the computational demands, necessitating powerful hardware like NVIDIA H100 GPUs and large data centers, driving the 'Gold Rush' for AI hardware.
After extensive training, LLMs like GPT-2 and the more modern Llama 3 emerge as 'base models.' These are essentially advanced token simulators, capable of generating text that statistically resembles their internet training data, but they are not yet 'assistants.' A model release typically includes the software code defining the neural network's architecture and the billions of learned parameters. Llama 3, a 45-billion-parameter model trained on 15 trillion tokens by Meta, is presented as a contemporary base model. Interacting with base models (e.g., via hyperbolic.ai) demonstrates their nature as glorified autocomplete rather than intelligent agents. Asking a base model 'What is 2+2?' often yields philosophical meandering or other statistical continuations, not a direct answer. However, these models store immense knowledge. Clever prompting, such as few-shot learning (e.g., providing example translations), can elicit specific knowledge or behavior. Even a rudimentary assistant can be simulated by structuring a prompt to mimic a human-AI conversation, leveraging the model's 'in-context learning' abilities.
The post-training phase transforms a base model (an internet document simulator) into an assistant capable of engaging in multi-turn conversations and answering questions. This computationally less expensive stage involves supervised fine-tuning (SFT) using curated datasets of conversations. These datasets are typically created by human labelers who are given conversational contexts and asked to provide ideal assistant responses, adhering to detailed 'labeling instructions' (e.g., be helpful, truthful, and harmless). LLMs are not programmed explicitly but implicitly through this data. The training works by continuing to train the base model, but now on this new dataset of human-crafted conversations. This rapid adjustment allows the model to learn the desired statistical patterns of assistant-like behavior. The conversation protocol involves special tokens (e.g., im_start, im_end) to demarcate user and assistant turns, ensuring the model understands the structured flow of dialogue. Modern SFT datasets often blend human-labeled and AI-generated synthetic conversations, reflecting an evolving approach to data collection.
Hallucinations, where LLMs generate factually incorrect information, are a significant challenge. This section explains their origin by showing how models, trained on confidently answered 'who is' questions, tend to maintain that confident tone even when asked about non-existent entities. This is because the model statistically imitates its training data's style without actual factual verification. Older models, like Falcon 7B, frequently hallucinate, offering varied fabricated answers to the same query. Mitigation strategy one involves explicitly training the model to recognize when it 'doesn't know.' Companies like Meta interrogate their models to identify the boundaries of their knowledge. For questions where the model empirically struggles, new training examples are added where the correct assistant response is to state 'I don't know' or 'I don't remember.' This teaches the model to associate internal 'uncertainty' activations with a verbal refusal to answer, thereby reducing confident but incorrect assertions. ChatGPT, a state-of-the-art model, demonstrates this by honestly stating it doesn't know about 'Orson Kovats' unless forced to use other tools.
Mitigation strategy two to combat hallucinations and enhance factuality is tool use. Similar to how humans might look up information they don't recall, LLMs can be equipped with tools like web search. This involves introducing special tokens (e.g., search_start, search_end) that the model can emit. When the inference program encounters these tokens, it pauses generation, performs the specified action (e.g., a web search), and injects the retrieved information back into the model's 'context window.' This context window acts as the model's 'working memory,' providing direct, explicit information for the current task, rather than relying on the 'vague recollection' stored in its parameters. Training for tool use involves creating datasets of conversations where the model learns by example when and how to invoke and utilize these tools. ChatGPT's ability to cite sources and conduct web searches is a prime example, effectively refreshing its working memory with up-to-date and verified information. This significantly improves accuracy, especially for factual queries.
LLMs' native computational capabilities are intrinsically linked to their token-based processing. The model processes tokens sequentially from left to right, with a finite and relatively small amount of computation occurring per token (analogous to a fixed number of layers in a Transformer model). This means models cannot perform complex calculations or extensive reasoning within a single token step. Therefore, for tasks requiring computation, it is crucial to 'distribute' the reasoning across multiple tokens. This implies that answers provided by training data should show intermediate steps, rather than just immediate final answers. Trying to force a model to output a complex answer in one token often leads to errors. A classic example is simple arithmetic problems: a model asked to provide an immediate answer might fail when numbers become slightly larger, whereas one that lays out its steps ('thinking out loud') consistently arrives at the correct solution. This highlights why guiding models to generate 'chains of thought' within their token stream is vital for complex problem-solving. It's essentially teaching them to use their limited per-token computational capacity effectively over a longer sequence.
This section further explores the 'jagged edges' of LLM cognition. Counting is another task where models struggle for the same reason as complex arithmetic: asking them to count a large number of items within a single token's processing (e.g., many dots in a string) overloads their per-token computational capacity. Using external tools like a code interpreter (Python's .count() function) circumvents this limitation. Similarly, spelling-related tasks are difficult because models primarily operate on tokens (text chunks) rather than individual characters. The tokenization process means they don't 'see' individual letters or their positions as easily as humans, causing issues with tasks like character manipulation or counting specific letters in a word. The famous 'strawberry' example illustrates this, where models historically miscounted the 'R's. When questioned about their own identity ('What model are you?'), LLMs often provide seemingly random or hallucinated answers. This is because they lack a persistent self and are merely statistical imitators of their training data, which might contain discussions about various AI models. Developers can 'hardcode' identity through specific training data or 'system messages' in the context window. However, these are external impositions, not intrinsic self-awareness. Lastly, surprisingly simple comparative tasks (e.g., 'Is 9.11 bigger than 9.9?') can stump LLMs, sometimes attributed to activating irrelevant patterns (like Bible verses), showcasing their unpredictable 'Swiss cheese' capability gaps despite advanced reasoning in other areas.
This section introduces Reinforcement Learning (RL) as the third major stage in LLM training, akin to a student working through practice problems after reading textbooks (pre-training) and studying worked solutions (SFT). While SFT teaches the model to imitate expert responses, RL allows it to discover optimal solutions and reasoning paths for itself. The core idea is simple: for a given problem (e.g., a math word problem), the model is prompted to generate multiple candidate solutions. Each solution is then evaluated (e.g., does it yield the correct numerical answer?). Solutions that lead to correct answers are 'reinforced,' meaning the model's parameters are adjusted to make those successful token sequences more likely in the future. Critically, RL does not rely on human-provided ideal *solutions* but rather on verifiable *outcomes* (e.g., the correct answer). This enables the LLM to explore and internalize problem-solving strategies optimized for its own cognitive architecture, which differs from human cognition. This iterative process of 'guess and check' allows the model to 'dial in' its reasoning, discovering more efficient and reliable ways to arrive at accurate answers over time, often by spreading out computations across tokens.
RL is not just about improving accuracy; it leads to the emergence of complex 'thinking' processes within LLMs. The Deep Seek R1 paper serves as a seminal example, demonstrating how RL significantly boosts a model's mathematical reasoning. Quantitative results show accuracy climbing with more RL steps. Qualitatively, these RL-trained models produce much longer responses, not because of verbosity, but because they learn to externalize their reasoning. They engage in self-correction: 'wait, let me reevaluate...''or 'let me check my math again,' or 'let me try a different approach.' This mirrors human problem-solving, revealing emergent 'chains of thought' that were not explicitly programmed but discovered through trial and error. This capability, where the model essentially learns *how* to think for itself, is a unique property of RL and dramatically improves problem-solving accuracy. Such 'thinking models' are available in services like chat.deepseek.com (with 'DeepThink' enabled) and in advanced paid tiers of ChatGPT (e.g., 01/03 models, which use 'advanced reasoning' via RL). This represents the frontier of LLM development, pushing beyond mere imitation to cultivate genuine problem-solving strategies.
While traditional RL (where correct answers are objectively verifiable, like in math or Go) can lead to superhuman performance, a variant called Reinforcement Learning from Human Feedback (RLHF) addresses 'unverifiable domains' like creative writing. In RLHF, humans don't provide perfect solutions but rather rank multiple AI-generated responses (e.g., jokes, poems) from best to worst. This human ranking is then used to train a 'reward model'—a separate neural network that learns to approximate human preferences. This reward model then serves as a 'simulated human' for subsequent RL, allowing for scalable, automated training without requiring humans to evaluate billions of outputs. The upside: RLHF allows RL in creative domains and empirically leads to better models. It leverages the 'discriminator-generator gap' (it's easier for humans to judge quality than to generate it from scratch). However, RLHF has a critical downside: the reward model, being a neural network itself, can be 'gamed.' RL is incredibly adept at finding 'adversarial examples'—inputs that inexplicably yield high scores from the reward model but are nonsensical to humans (e.g., 'the the the the' being rated as a top joke). This means RLHF cannot be run indefinitely like pure RL; extended training causes model performance to degrade as it optimizes for the flawed reward model rather than actual human preference. Therefore, RLHF is seen as a valuable fine-tuning step but not a magic bullet for achieving unbounded intelligence.
Looking ahead, LLMs are rapidly becoming multimodal, capable of processing and generating audio and images alongside text. This means tokenizing non-textual data and integrating these new token streams into the existing Transformer architecture, enabling natural conversations, seeing, and painting. Another key development is the rise of 'agents'—LLMs that can string together multiple tasks over longer periods, taking actions and self-correcting, albeit still requiring human supervision (leading to human-to-agent ratios in digital workflows). LLMs will also become more pervasive and invisible, integrated seamlessly into existing tools and infrastructure. From a research perspective, ongoing work explores 'test-time training,' allowing models to learn and adapt during inference, potentially overcoming limitations of the fixed context window and enabling more human-like, continuous learning. For staying updated, key resources include: 1) LLM Leaderboards (e.g., Elo Arena), which rank models based on human comparisons, although their reliability can vary. 2) AI News newsletters (e.g., by Swix and friends) offer comprehensive and curated summaries of the latest developments. 3) Platforms like X (Twitter) are hubs for real-time AI discussions and announcements. To use models, users can access proprietary models directly on providers' websites (e.g., ChatGPT, Google Gemini) or open-weights models (like DeepSeek, Llama) through inference providers (e.g., Together.ai, Hugging Face). Smaller, distilled models can even run locally on personal computers via applications like LM Studio.
The video concludes by reiterating the core understanding of LLMs. When a user interacts with ChatGPT, their query is tokenized and inserted into a conversation protocol. The model then acts as a sophisticated autocomplete, generating tokens based on its training. This process fundamentally involves three stages: 1) Pre-training, where the model acquires vast internet knowledge and stores it in its parameters. 2) Supervised fine-tuning (SFT), which imbues the model with personality and assistant-like behavior, primarily through human-curated datasets of ideal conversational responses following specific labeling instructions. Therefore, ChatGPT's output is akin to a statistical simulation of a highly trained human data labeler. 3) Reinforcement learning (RL), used by advanced 'thinking models' (e.g., ChatGPT's 01/03 models derived from DeepSeek R1 principles), perfects problem-solving by allowing the model to discover internal 'chains of thought' and reasoning strategies through trial and error—a process that goes beyond mere human imitation. Despite impressive capabilities, LLMs exhibit 'Swiss cheese' cognitive deficits: they can hallucinate, struggle with counting or simple comparisons, and have limits to their 'mental arithmetic.' The key takeaway is to view LLMs as powerful tools for inspiration and first drafts but always verify their output. It's an exciting time in AI, with continuous advancements towards multimodal capabilities, agents, and deeper learning paradigms, but critical engagement with these tools is essential for effective use.