Continual Learning Evaluation › Why Standard Evaluation Breaks Down

In a single-task setting, the evaluation ritual is simple: train the model, measure accuracy on a held-out test set drawn from the same distribution as the training data, and report that number. The score is a complete summary of model quality because the model only ever needs to handle one task. The test set is fixed, the training data is fixed, and the only question is how well the model generalizes from one to the other.

Continual learning trains the same model sequentially on tasks $T_1, T_2, \ldots, T_N$. After training on task $T_k$, a standard evaluation would report accuracy on a test set for $T_k$. This misses two critical questions:

• Has performance on earlier tasks $T_1, \ldots, T_{k-1}$ degraded?
• Did learning $T_{k-1}$ help or hinder learning $T_k$?

A model could score perfectly on $T_k$ while having catastrophically forgotten everything about $T_1$. That same model might also be harder to train on $T_k$ because the weight updates from $T_{k-1}$ moved parameters into a poorly suited region. Standard test accuracy captures neither of these dynamics.

Consider what happens when you fine-tune a large language model on a legal document classification task and then fine-tune it again on a medical summarization task. After the second fine-tuning, you might report high accuracy on medical summarization and declare success. But if you go back and test the model on legal classification, you may find the performance has collapsed. More subtly, you might ask whether the model learned medical summarization better or worse than a fresh model would have, given the pre-existing legal representations in its weights. Standard evaluation asks only the first question and ignores the second and third entirely.

The evaluation framework for continual learning therefore needs metrics that look backward at what was retained, look forward at what was transferred, and assess the full matrix of task performance over the entire learning sequence. Building this framework starts with a single data structure: the accuracy matrix.