Length Generalization with Log-Depth Recurrent Units
Researchers introduce MLP-LDRU, a recurrent architecture designed to overcome length generalization failures that plague both RNNs and transformers. By leveraging parallel reduction and associativity-biased operators, the model achieves near-perfect accuracy across regular language benchmarks when trained on longer sequences than baseline methods. This addresses a fundamental limitation in sequence modeling: the inability to reliably extrapolate beyond training distribution, which has implications for any task requiring compositional reasoning over variable-length inputs.
Modelwire context
ExplainerThe key insight is that log-depth reduction (not just parallel computation) is what enables length generalization. Most prior work assumed RNNs fail because they're sequential; this suggests the real bottleneck is how information combines across steps, not the computational path itself.
This connects directly to the continual learning work from earlier today on catastrophic forgetting. Both papers identify hard constraints in model capacity and learning: forgetting showed that saturation limits multitask adaptation regardless of replay strategy, while MLP-LDRU shows that standard architectures hit a wall on compositional extrapolation. The difference is scope. Forgetting is about sequential task learning; length generalization is about single-task robustness to distribution shift. Together they suggest the field is converging on the idea that architectural constraints, not just training procedures, determine what models can and cannot do.
If MLP-LDRU maintains near-perfect accuracy when tested on sequences 2x or 3x longer than the longest training example (not just slightly longer), that confirms the associativity bias is doing real work. If performance degrades sharply at some multiple, the paper's claims about compositional reasoning are overstated.
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MentionsMLP-LDRU · Log-Depth Recurrent Unit · RNN · Transformer
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