Building a good benchmark for continual learning takes a lot of thought -- it's non-trivial to make it hard in the ways that matter. excited to see people working towards this
Today, we’re releasing Continual Learning Bench 1.0: the first, realistic benchmark for measuring how AI systems can improve in online settings.
Benchmarks today assume models are stateless. Each example is independent, and once a system finishes a task, it moves on as if nothing happened.
But deployed AI systems should learn from experience. We tested 10+ frontier systems against novel, expert-validated tasks and find there’s still plenty of headroom for learning. (1/n)
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