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Artificial Intelligence

The Evolution of LLMs Through Real-Time Learning

Increasing "inference" makes LLMs smarter and better.

Key points

  • Traditional LLMs are largely static after training, lacking real-time learning and adaptation during use.
  • OpenAI's o1 Model actively learns during inference, evolving with each interaction for smarter responses.
  • The o1 Model excels in math, coding, and real-world tasks, staying relevant and adaptive in real time.
Art: DALL-E/OpenAI
Source: Art: DALL-E/OpenAI

There’s an important shift happening in the world of large language models (LLMs)—one that could redefine how we interact with artificial intelligence. And the answer, previewed today by OpenAi, might just lie in a new approach to how these models learn and adapt.

Let me explain.

The Old Way: Pre-Trained and Set in Stone

Traditionally, LLMs follow a linear training process. These models spend most of their life in pre-training, which involves digesting a massive amount of text data. This phase is computationally intensive, taking up the bulk of resources and time. After this comes post-training or fine-tuning, where the model is adjusted to specialize in certain tasks. Finally, you get to inference, where the model generates responses based on what it’s learned.

Here’s the catch: in most LLMs, the inference stage is pretty static. Once trained, the model doesn’t change much during use. It just regurgitates patterns based on its previous training without actively improving or adapting during interactions.

In other words, these models are like a well-educated person who can answer your questions—brilliantly so—but can’t learn anything new in real time. They might be highly competent, but there’s no ongoing growth as they interact with you.

Enter the OpenAI o1 LLM

Now, imagine an LLM that’s a bit more like a curious mind—one that doesn’t just give you static answers but learns, adapts, and evolves with each interaction. This is where OpenAI newly introduced o1 Model comes into play.

Unlike the traditional approach where inference is a tiny, static part of the model's life, the o1 Model treats inference as a dynamic, evolving process. During inference (the stage when you’re using the model), the o1 Model can actively learn and refine itself. This means it doesn’t just stop at pre-training and post-training—it keeps growing during use. It’s more responsive, more adaptive, and potentially more aligned with real-world complexities. The data from OpenAI are impressive.

In our tests, the next model update performs similarly to Ph.D. students on challenging benchmark tasks in physics, chemistry, and biology. We also found that it excels in math and coding. In a qualifying exam for the International Mathematics Olympiad (IMO), GPT-4o correctly solved only 13% of problems, while the reasoning model scored 83%. Their coding abilities were evaluated in contests and reached the 89th percentile in Codeforces competitions.

Why Does This Matter?

Think about it: in a world that’s changing faster than ever, static knowledge isn’t enough. We need models that can keep up. The o1 Model opens up possibilities for AI to become more intuitive and aligned with our real-time needs. It could mean smarter, more personalized health care diagnostics, more nuanced customer support, and even AI that evolves alongside its users in a way that feels more human than ever before.

Instead of simply relying on massive pre-training datasets that become outdated, models that can learn during use have the potential to stay current with the world around them. This represents a leap from "one-and-done" training to something much more dynamic.

The Future of AI: Learning in the Moment

So, what's next for AI? Models that don't just get smarter before they hit the market—but ones that keep evolving after. The traditional LLM approach might still work for static environments, but in a fast-paced, ever-changing world, the o1 Model hints at a future where AI learns in real time, adapts in real time, and becomes a more integral part of our lives.

It’s not just about making LLMs bigger or faster. It’s about making them smarter—and more responsive to the world around them.

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