Continual Learning in Transition

Hugging Face Daily Papers Papers
continual-learning survey adaptive-systems machine-learning ai-research

Summary

This paper surveys the evolution of continual learning from parameter-centric methods to system-level adaptation, proposing a tri-axial framework (When, How, Where) to characterize learning across pre-training, post-training, and inference stages.

Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.g., training strategies, architectural designs, and weight adaptation. However, emerging paradigms are reshaping the scope of CL beyond this traditional model adaptation view. For instance, on-policy learning broadens the space of update mechanisms; test-time training extends CL from the training phase to inference; and external harness components such as memory, skill libraries, and interaction protocols extend the evolutionary boundaries of model capabilities far beyond the static parameter space. Collectively, these developments indicate a transition from parameter-centric learning toward system-level adaptation. To characterize this transition, we examine the evolution of continual learning through three dimensions: When, How, and Where learning occurs. The How dimension encompasses off-policy, on-policy, and beyond-gradient optimization mechanics. The When dimension captures evolution across pre-training, post-training, and inference-time stages. The Where dimension delineates updates occurring within internal parameters versus external structural constraints. Anchored by this tri-axial framework, we systematically survey representative methods, trace the ongoing transition of continual learning, and discuss the key challenges, broader implications, and future directions arising from this paradigm shift.
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Abstract

Classicalcontinuallearning(CL)hasprimarilyfocusedonenablingmodelstoupdateandretainknowledgethroughparameter-centricmechanisms,e.g.,trainingstrategies,architecturaldesigns,andweightadaptation.However,emergingparadigmsarereshapingthescopeofCLbeyondthistraditionalmodeladaptationview.Forinstance,on-policylearningbroadensthespaceofupdatemechanisms;test-timetrainingextendsCLfromthetrainingphasetoinference;andexternalharnesscomponentssuchasmemory,skilllibraries,andinteractionprotocolsextendtheevolutionaryboundariesofmodelcapabilitiesfarbeyondthestaticparameterspace.Collectively,thesedevelopmentsindicateatransitionfromparameter-centriclearningtowardsystem-leveladaptation.Tocharacterizethistransition,weexaminetheevolutionofcontinuallearningthroughthreedimensions:When,How,andWherelearningoccurs.TheHowdimensionencompassesoff-policy,on-policy,andbeyond-gradientoptimizationmechanics.TheWhendimensioncapturesevolutionacrosspre-training,post-training,andinference-timestages.TheWheredimensiondelineatesupdatesoccurringwithininternalparametersversusexternalstructuralconstraints.Anchoredbythistri-axialframework,wesystematicallysurveyrepresentativemethods,tracetheongoingtransitionofcontinuallearning,anddiscussthekeychallenges,broaderimplications,andfuturedirectionsarisingfromthisparadigmshift.

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