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PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking

146 upvotes · 20 OCT 2026 · Pan Wang, Yihao Hu, Guangyuan Dong et al.

This paper proposes a new training method called Progress-Aligned Recurrence (PAR) to improve the performance of sequential state-tracking models on longer problems. By training the model to match the state after a certain number of operations and rescaling recurrent states, PAR enables models to generalize better to longer problems.

recurrent state trackingstate scalinglength generalizationcontinual learningsequence-to-sequence models