TL;DR: We can destroy a 67M-parameter language model's ability to predict German text by fine-tuning a single number: the scalar prefactor on one German-related rank-1 parameter subcomponent.
This is an early exploration into using parameter decomposition for a more targeted and interpretable form of model fine-tuning. At small German-token budgets, fine-tuning the scalar prefactor of a single German-related parameter subcomponent beats rank-1 and rank-4 LoRA [1] fine-tunes on the trade-off between German performance removed vs. English performance retained. The single scalar fine-tune reaches nats cross-entropy on German, the score you'd get from a uniform distribution over all output tokens, with nats cross-entropy increase to English over the base model, from as few as ~4 German training tokens, compared to tokens for the LoRAs.
In a sense this is cheating, though: we're indirectly exploiting the German tokens we already spent when we did the parameter decomposition and interpreted activating examples for the resulting subcomponents.
More interestingly, unlike the LoRAs, the scalar fine-tune consistently leaves French and Spanish almost untouched without us regularising for that. I found that out by accident. I didn't think to specify that performance on other languages should be retained, but the targeted nature [...]
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Outline:
(02:04) Recap: Parameter subcomponents
(03:31) Idea: fine-tune by rescaling existing subcomponents
(05:42) Original plan
(07:11) The selected subcomponents
(07:39) Results: the 16-component edit vs. rank-1 LoRA
(08:50) A happy accident
(11:13) A privilege of not working with black boxes
(14:56) Rollouts
(15:27) Limitations
(16:02) Acknowledgments
(16:36) Appendix A. More LoRAs
(16:41) Rank-4 LoRAs
(18:17) Localised rank-1 LoRAs
(19:41) Appendix B. Protocol and hyperparameters
The original text contained 4 footnotes which were omitted from this narration.
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First published:
June 25th, 2026
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Narrated by TYPE III AUDIO.
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