Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
arXiv:2511.08226v2 Announce Type: replace Abstract: In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome. The evaluation of continual learning methods relies on splitting a known homogeneous dataset and learning the associated tasks one after the other. We argue that most CL methods introduce a priori information about the data to come and cannot be considered agnostic. We exemplify this point with the case of methods relying on pretrained feature extractors, which are still used in CL.
The continuous push for more robust and autonomous AI systems highlights the current limitations of 'continual learning' methods that rely on pre-existing data assumptions.
This research directly addresses a foundational challenge in AI: creating systems that can truly learn new information continually without forgetting old knowledge, a vital step towards more capable AI agents.
The definition and evaluation criteria for effective continual learning methods will likely evolve, demanding more genuinely 'agnostic' approaches that do not pre-suppose future data patterns.
- · AI researchers focusing on foundational continual learning
- · Developers of truly agnostic AI architectures
- · Sectors requiring adaptable, long-lifecycle AI systems
- · AI methods reliant on static pre-trained feature extractors
- · Application areas expecting quick fixes from current 'continual learning'
- · Academia clinging to narrow benchmarks for CL
Increased focus and funding will be diverted toward developing genuinely agnostic continual learning algorithms.
This foundational progress could enable AI agents to operate more effectively in dynamic, unknown real-world environments.
The development of truly agnostic continual learning may accelerate the deployment of autonomous systems in complex scenarios, from robotics to decision-making AI.
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Read at arXiv cs.LG