The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
We investigate whether post-trained capabilities can be transferred across models without retraining, with a focus on transfer across different model scales. We propose the Master Key Hypothesis, which states that model capabilities correspond to directions in a low-dimensional latent subspace that induce specific behaviors and are transferable across models through linear alignment. Based on this hypothesis, we introduce UNLOCK, a training-free and label-free framework that extracts a capability direction by contrasting activations between capability-present and capability-absent Source variants, aligns it with a Target model through a low-rank linear transformation, and applies it at in...
| Authors | Rishab Balasubramanian, Pin-Jie Lin, Rituraj Sharma et al. |
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| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 5 May 2026 |
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