Stop Saying “It’s Just Next Token Prediction” (You Sound Like a 2023 Tutorial)

2026-03-04 · Source: To Data & Beyond · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The "Next Token Prediction" argument, which claims Large Language Models (LLMs) cannot truly reason and are merely "stochastic parrots," is outdated and technically inaccurate as of late 2024 to early 2025. Initially, models like GPT-3 (2020-2022) primarily mimicked text, often requiring Reinforcement Learning from Human Feedback (RLHF) and Proximal Policy Optimization (PPO) to align responses with human preferences via a "Reward Model." This approach, while making models polite, still led to "people pleaser" AIs that optimized for approval over truth. By 2024, Direct Preference Optimization (DPO) emerged, streamlining the process by directly integrating human preferences into the model's loss function, eliminating the need for a separate "Critic" model. However, the significant shift to true reasoning began in late 2024 and early 2025 with algorithms like Group Relative Policy Optimization (GRPO), which fundamentally changed the underlying cognitive architecture from imitation to optimization, enabling models to reason rather than just predict preferred structures.

Key takeaway

For AI Scientists evaluating current LLM capabilities, recognize that the "Next Token Prediction" argument is obsolete. Your understanding of AI's future should focus on advanced optimization techniques like RLHF, DPO, and GRPO, which enable reasoning beyond simple prediction. Investigate how these architectural shifts impact model behavior and potential applications, moving past outdated conceptualizations of LLM intelligence.

Key insights

Modern AI's shift from imitation to optimization, via RLHF, DPO, and GRPO, enables reasoning beyond mere next token prediction.

Principles

• AI evolution moved from imitation to optimization.
• Direct preference integration enhances model understanding.

Method

RLHF used a "Reward Model" with PPO for human feedback. DPO directly integrates preferences into the loss function. GRPO facilitates reasoning capabilities.

In practice

• RLHF aligns model output with human approval.
• DPO simplifies preference learning by removing the critic model.

Topics

• LLM Optimization
• Reinforcement Learning from Human Feedback
• Direct Preference Optimization
• Group Relative Policy Optimization
• AI Reasoning

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by To Data & Beyond.