Beyond Static Intelligence: Evaluating Continual Learning — Parth Asawa, UC Berkeley

Current language model evaluation relies on independent, stateless tasks that ignore a model's ability to learn over time. This paradigm fails to measure "continual learning," defined as stable, sample-efficient online adaptation. A robust evaluation framework requires three fundamental criteria: headroom for online adaptation, shared latent structure across tasks, and clear feedback mechanisms. By introducing the "gain" metric—the performance difference between stateful and stateless systems—researchers can isolate genuine learning from base model capabilities. Initial results from the Continual Learning Bench 1.0 reveal that current systems frequently struggle with the stability-plasticity trade-off, often failing to retain prior knowledge or adapt to concept drift. Prioritizing continual learning as a first-order design requirement rather than an afterthought is essential for developing models that evolve through experience rather than relying on static, frozen checkpoints.