Learning Model Successors
This work addresses the foundational challenge of generalization in machine learning, shifting from task-specific methods to a unified framework for induction, though it is incremental as it builds on existing concepts.
The paper tackles the problem of out-of-domain generalization by formalizing inductive generalization along difficulty progressions, proposing a novel learning paradigm called Inductive Learning with model successors to unify fragmented approaches.
The notion of generalization has moved away from the classical one defined in statistical learning theory towards an emphasis on out-of-domain generalization (OODG). There has been a growing focus on generalization from easy to hard, where a progression of difficulty implicitly governs the direction of domain shifts. This emerging regime has appeared in the literature under different names, such as length/logical/algorithmic extrapolation, but a formal definition is lacking. We argue that the unifying theme is induction -- based on finite samples observed in training, a learner should infer an inductive principle that applies in an unbounded manner. This work formalizes the notion of inductive generalization along a difficulty progression and argues that our path ahead lies in transforming the learning paradigm. We attempt to make inroads by proposing a novel learning paradigm, Inductive Learning, which involves a central concept called model successors. We outline practical steps to adapt well-established techniques towards learning model successors. This work calls for restructuring of the research discussion around induction and generalization from fragmented task-centric communities to a more unified effort, focused on universal properties of learning and computation.