Next-Token Prediction: How LLMs ACTUALLY Think

Описание: Ever wonder how Large Language Models actually generate text, write code, or solve complex math problems? It all comes down to a deceptively simple mechanism: Next-Token Prediction (NTP).

In this video, we break down the core engine behind models like ChatGPT, Claude, and Llama—from the basic mechanics of tokenization to advanced concepts like Softmax, KV Caching, and GRPO.

What You’ll Learn:

The Fundamentals: How text is broken down into tokens (Byte-Pair Encoding) and processed through a context window.

The Math Behind the Magic: How raw logits become probabilities using Softmax, temperature, and cross-entropy loss.

Decoding Strategies: The difference between Greedy Search, Top-K, Top-P (Nucleus) sampling, and Beam Search.

Why "Autocomplete" Reasons: How a simple prediction objective leads to complex skills like logic, grammar, and multi-step reasoning.

Optimizations & Advanced Topics: KV Caching, Speculative Decoding, Constrained Decoding, Vision Tokenization, and Group Relative Policy Optimization (GRPO).

Limitations: Why NTP leads to hallucinations, lack of global planning, and high computational costs.

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