arXiv:2509.24510v3 Announce Type: replace 
Abstract: Recent empirical studies have explored the idea of continuing to train a model at test-time for a given task, known as test-time training (TTT), and have found it to yield significant performance improvements. However, there is limited understanding of why and when TTT is effective. Earlier explanations mostly focused on the observation that TTT may help when applied to out-of-distribution adaptation or used with privileged data. However, the growing scale of foundation models with most test data being in-distribution questions these explanations. We instead posit that foundation models remain globally underparameterized, with TTT providing a mechanism for specialization after generalization, focusing capacity on concepts relevant to the test task. Specifically, under the linear representation hypothesis, we propose a model in which TTT achieves a substantially smaller in-distribution test error than global training. We empirically validate our model's key assumptions by training a sparse autoencoder on ImageNet, showing that semantically related data points are explained by only a few shared concepts. Finally, we perform scaling studies across image and language tasks that confirm the practical implications of our model, identifying the regimes where specialization is most effective.

أظهرت الدراسات الحديثة فعالية التدريب في وقت الاختبار (TTT) في النماذج الأساسية، مما يشير إلى أن الاستمرار في تدريب نموذج أثناء الاختبار يمكن أن يؤدي إلى تحسينات كبيرة في الأداء. يُفترض أن هذه الطريقة تسمح للنماذج بالتخصص بعد التعميم، مع التركيز على مفاهيم ذات صلة بالمهام المحددة.

Estudios recientes han destacado la efectividad del entrenamiento en tiempo de prueba (TTT) en modelos fundamentales, sugiriendo que continuar entrenando un modelo durante las pruebas puede llevar a mejoras significativas en el rendimiento. Este enfoque se postula como una forma de permitir que los modelos se especialicen después de la generalización, adaptándose a tareas específicas mientras se enfocan en conceptos relevantes.

Des études récentes ont mis en évidence l'efficacité de l'entraînement en temps de test (TTT) dans les modèles fondamentaux, suggérant que continuer à entraîner un modèle pendant les tests peut conduire à des améliorations significatives des performances. Cette approche permettrait aux modèles de se spécialiser après la généralisation, en s'adaptant à des tâches spécifiques tout en se concentrant sur des concepts pertinents.

Recent studies have highlighted the effectiveness of test-time training (TTT) in foundation models, suggesting that continuing to train a model during testing can lead to significant performance improvements. This approach is posited to allow models to specialize after generalization, particularly in adapting to specific tasks while maintaining a focus on relevant concepts.

Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models

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Specialization after Generalization: Towards Understanding Test-Time Training in Foundation Models

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