Part II: From the Teacher as Gradient to Attention

In the previous post, The Teacher as Gradient, I wrote about how learning really happens not at the final answer, but by tracing mistakes backward and correcting them with direction and proportion. That idea helped me understand backpropagation not as an algorithm, but as a philosophy of learning.

But backpropagation explains only how systems improve after they are wrong.

It does not explain something equally important: how systems decide what to focus on before they respond.

That is where attention comes in.

Before Correction Comes Focus

When a teacher corrects a student, they don’t correct everything at once. They focus on the part that matters most right now. A wrong assumption. A skipped step. A misunderstanding that, if fixed, will unlock the rest.

Before correction, there is focus.

Transformers work the same way.

Query: What the Student Is Trying to Understand

In a classroom, confusion is not noise it is a signal. When a student gets stuck, their attention narrows. They are no longer absorbing the entire lecture. Internally, a question forms: where did this step come from?

That moment of intent is the query.

In a Transformer, the query represents what the model is trying to understand at a given moment. It is shaped by everything seen so far. Each token forms its own query, silently asking: what should I pay attention to next?

Just like a student, the model does not search blindly. It searches with purpose.

Key: What the Teacher and Context Offer

Around the student, there is a lot of information. Definitions on the board. Earlier assumptions. Examples. Side explanations.

Each of these signals advertises what it is about.

These are the keys.

In Transformers, keys describe the identity of information. They do not answer questions they announce themselves. When a query meets a key, relevance emerges. Some explanations resonate strongly with the student’s confusion. Others barely register.

A good teacher senses this instantly. They repeat a definition. They revisit an assumption. They skip what doesn’t matter right now.

That relevance matching is attention.

Value: What Actually Changes Understanding

Eventually, the teacher explains the missing idea in just the right way. Not everything only what matters. That explanation lands. The student’s understanding shifts.

That shift is the value.

In Transformers, values carry the information that actually flows forward once relevance is decided. Keys help decide where to look. Values determine what is taken.

This separation matters. Two explanations might be equally relevant, but the understanding they provide can be very different. Attention allows the system to extract meaning, not just relevance.

How This Completes the Picture

Backpropagation explains how learning improves after an error. Attention explains how focus is chosen before a response.

Together, they form a complete learning loop:

• Attention decides what matters right now
• Backpropagation decides how to improve next time

In human terms:

• Attention is the teacher deciding what to correct
• The gradient is the teacher deciding how strongly to correct it

Transformers do both.

They focus first. They adjust later.

A Familiar Pattern, Seen Clearly

Long before I studied neural networks, I experienced this cycle in classrooms. Confusion shaped attention. Attention shaped explanation. Explanation reshaped understanding. Correction refined thinking.

Once I saw Transformers through this lens, attention stopped feeling like a mechanism and started feeling like judgment.

Not judgment in the moral sense but judgment in the learning sense.

The ability to decide what matters.

Backpropagation taught me that learning is about being correctable. Attention taught me that learning is about knowing where to look.

Together, they explain why Transformers don’t just process information.

They learn how to focus.

Thanks Sreeni Ramadorai