Looped GPT-BERT: Trading Parameters for Computation in Small Language Modeling
When training data are limited, increasing parameter count is not the only way to improve language-model performance. A small parameter set, when repeatedly applied, can also deliver comparable performance. We study Looped GPT-BERT in the BabyLM 2026 Strict-small setting, combining GPT-BERT's masked next-token and causal language-modeling objectives with depth-wise parameter sharing. We train on a preprocessed 7.48M-word English corpus and compare objective ratios, non-looped and looped architectures, and loop counts. Our final $4\times12$ model uses four physical layers for twelve recurrent traversals and contains 12.18M parameters. The BabyLM 2026 leaderboard reports an Overall Average ...
| Authors | Tingshuo Fan, Hongtao Mu, Tianyu Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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