# How GravityLLM Learns: Training an AI to Place Sound in Space

> Learn how GravityLLM learns to place sound in 3D: supervised learning, the training loop, loss curves, overfitting, learning rate and data ethics.

Web page: https://spatial9.ai/blog/academy-how-gravityllm-learns
Author: Spatial9 Team
Published: 2026-10-04. Updated: 2026-10-04.

## Key takeaways
- GravityLLM learns by supervised learning, pairing stems, genre and a prompt with what a skilled engineer actually did in a real session.
- The training loop has four steps: sample an example, predict positions, compare against the engineer's choice to get the loss, and adjust the weights.
- Overfitting shows when train loss keeps falling while validation loss bottoms out and rises, and the usual fix is keeping the best checkpoint.
- A learning rate that is too high makes loss jump around, while too few training steps cause underfitting and too many may cause overfitting.

In this lesson: You will learn how an AI model like GravityLLM learns to place sound in space. We cover supervised learning, the training loop, loss, train and validation curves, overfitting and learning rate, then look at why human briefs and data ethics matter just as much as the math.

Think back to how you learned to mix. Nobody handed you a rulebook that said "put the kick here and the vocal there." You listened to records you loved. You watched experienced engineers work. You tried something, compared it with a reference, and adjusted. Over hundreds of sessions, patterns settled into your ears.

An AI model learns in a surprisingly similar way. It sees many examples, makes a guess, checks how far off it was, and corrects itself a tiny bit. Then it does it again, and again, many thousands of times.

In the previous lesson, you met the training lane of the Spatial9 pipeline. Now we slow it down and look inside. You do not need to be a data scientist to follow along. You only need the curiosity you already bring to the studio.


## What you will learn

- How supervised learning turns real studio sessions into training examples.
- The four steps of the training loop and what loss measures.
- How to read train and validation loss curves and spot overfitting.
- What learning rate and training steps control.
- Why human briefs and data ethics shape what a model can become.

The rest of this lesson is free with a Spatial9 account: https://spatial9.ai/blog/academy-how-gravityllm-learns