How is this different from auxiliary loss?

Auxiliary tasks are the helper tasks; the auxiliary loss is the training term that measures them. Usage overlaps.

What is negative transfer?

When training on another task makes the main task worse.

Is multi-task learning only for big models?

No. It can help small models with limited data too.

Do auxiliary tasks cost extra at prediction time?

Usually not. The extra outputs are removed after training, so the deployed model is the same size as a single-task one.

AI Glossary / AI and machine learning / Advanced

Auxiliary Tasks

Definition

Auxiliary tasks are extra learning tasks, related to the main one, that a model is trained on at the same time, to help it learn representations that improve performance on the main task.

What it means

In multi-task learning, a model is trained on several tasks together. When one task is the real goal and the others are there to help, the helpers are called auxiliary tasks.

Ruder's overview of multi-task learning describes why this can work: the tasks share a representation, so what is learned for one can help another. Extra tasks can act as a form of regularization, add data, and focus attention on features that matter.

Typical auxiliary tasks are cheap to get labels for. A parser may predict part-of-speech tags, a driving model may predict lane markings, and a sentiment model may predict related emotions.

Choice matters. A related task can help, while an unrelated one can hurt, a problem called negative transfer.

For a business, auxiliary tasks matter mainly as a reason why vendors train models on broader data than your specific use, and as an idea for squeezing more from limited labels.

A classic example in language research is training a model on an easy, abundant task such as judging whether a sentence is grammatical, alongside a harder task with little data. The easy task teaches structure that the hard task reuses.

How it works

A shared network learns a common representation, with separate output layers for the main and auxiliary tasks. All tasks are trained together, with the combined loss updating the shared layers.

Because several tasks must be served by one representation, the network is pushed to learn features that are general, not just those that fit the noise of one small dataset.

The auxiliary tasks are chosen for relatedness: they should depend on similar underlying features. Researchers test candidates by measuring main-task performance with each one added.

At deployment, the auxiliary outputs are often dropped. The benefit is in the improved shared representation, not in using the auxiliary predictions.

Three practical examples

Language: predicting the next word or the part of speech while learning a harder task such as named-entity recognition.
Vision: predicting depth or edges while learning object recognition.
Business data: predicting a related customer behavior, such as email opens, to help a model predicting renewals.

Think of it this way

Think of learning Spanish. You study vocabulary, grammar and pronunciation, which are separate drills, but each supports your real goal of holding a conversation.

The limit: drilling something unrelated, such as chess openings, will not help your Spanish and takes time from it.

Worked example

choosing helper tasks for a support classifier

Suppose a company wants to classify support tickets by urgency, but has only a few hundred labeled examples. It has far more tickets labeled by product area.

It trains one model with two outputs: urgency as the main task and product area as the auxiliary task. Shared layers must serve both.

It compares the multi-task model with an urgency-only model on the same held-out tickets. The numbers are illustrative; the comparison is the point.

If urgency improves, product area stays as a helper. If not, it is removed, as it may be competing rather than helping.

Illustrative case study

A fictional logistics firm trains a model to predict delivery delay and adds auxiliary tasks for weather category and route type, both of which were already recorded.

Delay predictions improve a little on routes with little history. A third auxiliary task, driver shift length, lowers accuracy, and the team drops it.

The invented lesson: auxiliary tasks need testing one by one, because a poor choice can make things worse.

Common misconceptions and mistakes

More tasks always means a better model. Unrelated tasks can hurt through negative transfer.
Auxiliary predictions are the product. They are usually only training aids.
It is only for neural networks. The idea is general, though it is most common with them.

Business use

Consider auxiliary tasks when your main task has little labeled data but related labels are plentiful.

Pick helpers that depend on similar information, test them individually against a single-task baseline, and drop those that do not help.

This is one reason large pre-trained models are useful starting points: they were trained on broad auxiliary tasks, so a little of your data goes further than starting from scratch.

Check your understanding

A team adds a helper task and the main metric falls. What is a likely explanation?

Answer: Negative transfer: the helper task is not related enough, or is weighted too heavily, so it pulls the shared features away from what the main task needs.

Related terms

Auxiliary loss
Transfer learning
Regularization
multi-task learning
shared representation
negative transfer

Learn next

AI for Business Owners helps you ask the right questions about how AI products are trained: https://aiforbusinessandlife.com/academy/ai-for-business-owners. [C2]

Sources

[S17] Ruder, An Overview of Multi-Task Learning in Deep Neural Networks; [S2] Google, Machine Learning Glossary. Full URLs are in the source ledger. Verified 11 October 2026.

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