Extending benefits of randomized positional encodings to other tasks

Investigate whether stochastic (randomized) positional encoding schemes confer advantages such as improved length generalization and trainability for transformer models on tasks beyond the q-sparse token selection task, and characterize the scope and limitations of these benefits across different problem settings.

Background

The work demonstrates that stochastic positional encoding facilitates both convergence and strong length generalization for the q-sparse token selection task, and provides theoretical and empirical support for these benefits.

The authors ask whether these advantages persist for other tasks and architectures, motivating a systematic paper to identify when and why randomized positional encodings help, and to formalize the conditions under which such benefits extend beyond the specific task analyzed.