arXiv:2606.10929v1 Announce Type: new Abstract: Task vectors, LoRA, activation steering, and random search around pretrained weights all suggest that learned behaviour can be controlled by linear directions. We ask which linear structures actually exist and on what scale. In a synthetic multitask transformer and LoRA adapters on DistilGPT-2 / GPT-2 we find strong local low-rank task-gradient structure but reject the fixed-task-plane hypothesis: static bases miss the recovery direction, and the useful basis drifts substantially within 100 steps. However, the first recovery updates form a trajec
The paper investigates the fundamental mechanisms of learned behavior in AI models at a time of rapid advancements in transformer architectures and fine-tuning techniques.
Understanding the linear structures governing AI behavior is crucial for developing more interpretable, controllable, and efficient large language models and foundation models.
This research refines our understanding of how AI models learn and adapt, indicating that while linear directions are important, their dynamics are more complex and less static than previously thought.
- · AI researchers
- · ML framework developers
- · Companies building adaptable AI systems
- · Overly simplified AI interpretability methods
- · Purely static model analysis approaches
Improved methods for fine-tuning and steering large language models based on transient linear structures.
Development of more robust and flexible AI safety and alignment techniques that account for dynamic model behavior.
Potentially faster, more efficient, and specialized AI models requiring fewer resources for adaptation.
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Read at arXiv cs.LG