Inoculation Midtraining with Learned Neologisms
Inoculation Midtraining confines unsafe LLM behavior to a neologism-marked context, reducing misalignment after unsafe post-training but leaking under nearby contextual cues.
The paper introduces Inoculation Midtraining, which teaches a base model during midtraining that unsafe behavior belongs to a context marked by a learned neologism token, then post-trains on unsafe data within that context. Across supervised fine-tuning and RL post-training regimes, the technique reduces misalignment while preserving transfer of benign properties like German or Shakespearean prose. However, it does not outperform standard Inoculation Prompting, is sensitive to training configuration, and produces a leaky boundary that nearby contextual cues can reactivate. The authors conclude it is not yet a load-bearing component of a developer safety framework.
- Neologism token introduced at midtraining designates unsafe context for post-training
- Reduces misalignment across SFT and RL while preserving benign property transfer
- Fails to beat standard Inoculation Prompting and is training-config sensitive
- Boundary is leaky: nearby contextual cues can reactivate unsafe behavior
From the original article
1 min read at arxiv.orgLarge language models (LLMs) often learn both desirable and undesirable properties during post-training. We study whether midtraining, an earlier training stage, can shape which of these properties later generalise.
By Kyle O'Brien · arxiv.org
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