New Benchmark Probes Transformer Generalization Over Task Complexity
jasondeanlee · x · 2026-08-04
The thread argues that we need more physics-of-language-models style benchmarks to probe the limits of different architectures and the co-design of objectives and optimizers.
The linked paper, Adaptivity and Modularity for Efficient Generalization Over Task Complexity (arXiv:2310.08866), studies whether transformers can generalize across problems with varying difficulty. The authors propose tasks based on pointer value retrieval and find that standard transformers struggle when the number of sequential computation steps increases.
Their proposed model, Hyper-UT, combines:
- dynamic function generation from hypernetworks
- adaptive depth from Universal Transformers
According to the abstract, this improves accuracy and allocates computation more fairly on harder cases. The paper also claims the idea transfers beyond toy tasks, including a standard image recognition setting.
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