Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks

2026-07-20

AI Summary

This work investigates whether the standard Transformer architecture is universally optimal across all tasks and proposes an architectural refinement that introduces task-specific inductive biases through learnable nonlinear components, such as GeLU or softmax. The approach preserves the original Transformer structure while replacing key activation functions with task-optimized counterparts learned during training. Experimental results demonstrate that this modification substantially improves learning speed, in- and out-of-distribution generalization, and training stability on algorithmic reasoning tasks. Consistent, albeit more modest, performance gains are also observed in language and code modeling, accompanied by enhanced cross-domain transfer capabilities. These findings indicate that the standard Transformer is not locally optimal for specific tasks and that incorporating task-tailored design elements—despite a trade-off in generality—can effectively boost performance.

Abstract

Transformers are remarkably versatile and their design is largely consistent across a variety of applications. But are they optimal for any given task or dataset? The answer may be key for pushing AI beyond merely scaling current designs. *Method.* We present a method to optimize a transformer architecture for a given dataset, which we use as a tool to study optimal task-specific inductive biases. This method replaces the most important non-linearities (GeLUs,;softmax) with functions learned on held-out data. We then train the resulting architectures on other datasets, as a way to evaluate the compatibility between pairs of tasks. Findings. On algorithmic toy tasks, we identify new architectures with dramatic improvements in learning speed, in- and out-of-distribution generalization, and stability across seeds. The new designs prove very task-specific however, and indicate that these tasks require inductive biases very different from those of standard transformers. On code and language modeling datasets, we also find architectures with consistent, yet smaller improvements. These designs transfer much better across datasets and domains (English & computer code). Implications. Our results show that standard transformers are rarely a local optimum in the space of architectures. Simple alternatives can perform much better but sacrifice universality. This suggests that there may be room for improved architectures that better support multiple capabilities simultaneously, such as fluency and robust reasoning.

Problem

• Transformers
• inductive biases
• task compatibility
• architecture optimization
• generalization

Innovation

• task-specific inductive biases
• learnable nonlinearities
• architecture optimization
• cross-task compatibility
• Transformer variants