Continual Context Compression: Training Models to Learn Continually at Test Time

We formalize test-time continual learning (TTCL), in which models continually acquire, retain, and use information from an open-ended deployment stream. We focus on observation-driven TTCL, where future queries are unknown during learning. The de facto solution, continual in-context learning (ICL), retains deployment history in context, causing memory and inference costs to grow with experience. We instead argue that models should be explicitly trained before deployment to learn continually at test time. Our training algorithm, Continual Context Compression (C3), combines long-context data, context distillation, and bounded memory to learn a state-update rule. The resulting algorithm performs test-time compression: it consolidates evicted context into persistent state in a single forward pass, without deployment-time supervision generation or gradient descent. Across repeated updates, it outperforms gradient-based sleep-time methods with orders of magnitude less deployment compute. Scaling to Qwen3-8B, C3 outperforms matched-budget memory baselines on future-use tasks, with strong acquisition and retention and limited interference with existing capabilities. Moreover, on held-out label-learning tasks, its state can even outperform continual ICL within the model's native context range. These results support TTCL as a first-class objective of model development; models should be trained not only for what they know, but for how effectively they learn afterward.