Artificial Intelligence · 18.08.2026, 13:55 UTC
A Reproducibility Study of Partial Residual Ablations in Pre-LN Transformers
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.LG ↗ |
| Veröffentlicht | 18.08.2026 UTC |
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arXiv:2608.14689v1 Announce Type: new Abstract: Residual connections are a fundamental component of transformer architectures, yet the roles of the attention and feed-forward residual pathways remain poorly understood when considered independently. This paper presents a reproducibility study of partial residual ablations in Pre-LN GPT-style transformers trained at two scales (10M and 124M parameters). I compare four architectural configurations by selectively removing the attention residual connection, the feed-forward residual connection, or both. Across all experiments, removing the attention residual (FFNOnly) consistently causes deterministic collapse to the No-Residual performance floor. In contrast, removing the feed-forward residual (AttnOnly) exhibits a reproducible recovery effect at 10M scale under a controlled 8-seed deterministic study, while its behavior at 124M remains unresolved because of substantial seed variance. During the investigation, I identified and corrected an experimental measurement confound in runtime gain scaling and document both the failed intermediate reproduction and the subsequent controlled replication. Based on the empirical results, I propose a cross-position routing hypothesis to explain the observed asymmetry while explicitly distinguishing confirmed findings from unresolved questions. To support reproducibility, I release the complete source code, experiment configurations, checkpoints, training logs, and all experimental results, including intermediate non-reproducing runs.