Topic 43: What is Meta-Learning?

What sets a brilliant mind apart? The ability to learn how to learn. It’s the secret sauce for humans who thrive, navigating life’s challenges with ease. The same holds true for intelligent systems. As humans we develop this skill – often without even noticing – through school, university, and life’s endless lessons, learning to recognize familiar patterns across tasks that help us pick up new things more effectively. Models can acquire a similar ability too, through a process called meta-learning.

Meta-learning is the key to fast, flexible, and efficient adaptation of models to new, unseen tasks with minimal data. It allows models to learn from a few examples, gain experience, and use their memory effectively. Meta-learning isn’t on the same level as supervised, unsupervised, or reinforcement learning – it’s a higher-level framework that can be applied on top of them.

Today, we’re going to explore the basics of meta-learning, the most fascinating up-to-date developments (the one super exciting is what not to learn), how meta-learning helps with evals (meta-evaluation), and more (Brain In-Context, anyone?). It’s a lot to unpack! But first →

In today’s episode, we will cover:

• How it all started: The first mentions of meta-learning

• How does meta-learning work?

• Common meta-learning approaches

  • Optimization-based meta-learning

  • Metric-based meta-learning

  • Model-based meta-leaning

• Recent advances in meta-learning

  • Robustly Informed Meta Learning (RIME)

  • Meta-LoRA

  • Reinforced Meta-thinking Agents (ReMA)

  • Meta-evaluation

• General limitations

• Conclusion

• Sources and further reading

How it all started: The first mentions of meta-learning

The idea of adaptive systems and machines that could modify their own instructions emerged back in 20th century. But the pioneer of bringing the concept of learning to learn into neural networks and modern meta-learning frameworks was – ta-da-dam – Jürgen Schmidhuber.

In the work "Evolutionary principles in self-referential learning" (1987), he described self-improving systems that anticipated some aspects of meta-learning. In "A self-referential weight matrix" (1993) and "Reducing the ratio between learning complexity and number of time-varying variables in fully recurrent nets" (1993), he proposed architectures where one Recurrent Neural Network (RNN) modifies the weights of another RNN – it is an early form of gradient-based meta-learning (we will clarify what it is further).

Then in 1998, a book “Learning to Learn” by Sebastian Thrun and Lorien Pratt was among the first to put together various methods and ideas under the meta-learning umbrella. After that, in 2000 Jonathan Baxter published a paper, called "A Model of Inductive Bias Learning," which provided a PAC-learning (Probably Approximately Correct learning) framework. He showed that if you train on many tasks from the same family, then you can learn a useful inductive bias (a kind of prior knowledge) that helps you learn new tasks faster.

And that brings us to what meta-learning is all about. It is a concept where a model is trained on many tasks, rather than one single task, so that it can quickly adapt to new tasks using only a small amount of data. Few-shot image classification is a popular example of how meta-learning performs – after meta-learning, a model can learn to classify new categories from only a few training images.

Meta-learning entire idea conceptually differs from other learning approaches. For example, supervised learning aims to train a model to perform well on a single specific task using labeled data; unsupervised learning works without labels, seeking patterns, clusters, or latent structures within raw input data, and the model keeps learning from the dataset; and finally, reinforcement learning (RL) teaches an agent to act in an environment through trial and error by maximizing its reward over time.

Meta-learning is a framework rather than a specific type of learning. It doesn't rely on large datasets per task for fast adaptation. Instead, it’s about improving a model’s ability to adapt quickly to new tasks by training it across many tasks. These tasks can be supervised (e.g. few-shot classification), reinforcement-based (e.g. learning policies faster), or unsupervised (e.g. learning to cluster or represent data efficiently). Another key point is that meta-learning enables models to apply learned skills across different scenarios. A few concrete meta-learning task →

1. Spotting Rare Animals
   Show the model five pangolins. Then ask if a new photo is a pangolin.
   Meta-learning helps it decide – with just a few examples.

2. Teaching a Robot New Tricks
   The robot has opened drawers and turned knobs. Now it needs to pull a lever.
   Thanks to meta-learning, it adapts fast.

3. Adapting to a New Writing Style
   An AI assistant sees just 2–3 emails from a new user.
   Meta-learning lets it mimic their tone almost instantly.

Let’s unfold how the actual workflow of the meta-learning process looks.

How does meta-learning work?