Annotations Mitigate Post-Training Mode Collapse
Post-training (via supervised fine-tuning) improves instruction-following, but often induces semantic mode collapse by biasing models toward low-entropy fine-tuning data at the expense of the high-entropy pretraining distribution. Crucially, we find this trade-off worsens with scale. To close this semantic diversity gap, we propose annotation-anchored training, a principled method that enables models to adopt the preference-following behaviors of post-training without sacrificing the inherent diversity of pretraining. Our approach is simple: we pretrain on documents paired with semantic annotations, inducing a rich annotation distribution that reflects the full breadth of pretraining data...
| Authors | Jacob Mitchell Springer, Madhu Advani, Lukas Aichberger et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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