PL-Reg adds a trainable mask and a defined/undefined classification loss to logic-based regularization, improving unknown-class accuracy across GCD, mDG+GCD, and CIL benchmarks.
A Theory for Length Generalization in Learning to Reason
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Length generalization (LG) is a challenging problem in learning to reason. It refers to the phenomenon that when trained on reasoning problems of smaller lengths or sizes, the resulting model struggles with problems of larger sizes or lengths. Although LG has been studied by many researchers, the challenge remains. This paper proposes a theoretical study of LG for problems whose reasoning processes can be modeled as DAGs (directed acyclic graphs). The paper first identifies and proves the conditions under which LG can be achieved in learning to reason. It then designs problem representations based on the theory to learn to solve challenging reasoning problems like parity, addition, and multiplication, using a Transformer to achieve perfect LG.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Saving for the future: Enhancing generalization via partial logic regularization
PL-Reg adds a trainable mask and a defined/undefined classification loss to logic-based regularization, improving unknown-class accuracy across GCD, mDG+GCD, and CIL benchmarks.