Scaling Probabilistic Transformer via Efficient Cross-Scale Hyperparameter Transfer
Probabilistic Transformer (PT), a white-box probabilistic model for contextual word representation, has demonstrated substantial similarity to standard Transformers in both computational structure and downstream task performance on small models and small to medium sized datasets. However, PT is less robust to hyperparameter choices than standard Transformers, making it harder to scale efficiently. In this work, we follow Maximal Update Parametrization (muP) to rescale PT's parameters, so that hyperparameters optimized on small models can be transferred to larger models without additional tuning. With this approach, we successfully scale PT to models with up to 0.4B parameters. Experiments...
| Authors | Penghao Kuang, Haoyi Wu, Kewei Tu |
|---|---|
| Primary category | cs.CL |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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