🤖 AI Summary
This study addresses whether layer intervention responses during language model pretraining reflect persistent organizational structures or transient training effects. To investigate this, the work employs single-block identity bypasses and fixed teacher-forced contexts to conduct a longitudinal comparative analysis across multiple checkpoint trajectories. The findings reveal that the depth-wise ranking of layer sensitivity remains persistently stable throughout training, whereas intervention magnitudes are dynamically redistributed. Furthermore, the balancing mechanism between local missing updates and downstream responses varies across models. These results demonstrate that longitudinal layer sensitivity is structured yet non-static, thereby challenging the generalizability of intervention-based conclusions drawn from single checkpoints.
📝 Abstract
Layer interventions are widely used to probe the internal organization of language models, yet most analyses examine a single training checkpoint even though model representations and computations evolve throughout pretraining. This leaves open which depth-dependent intervention responses reflect persistent organization and which are transient consequences of training. We study this question using single-block identity bypass on fixed teacher-forced contexts across five released trajectories and 11 model-domain combinations. We find that block-bypass responses retain recognizable depth ordering while their magnitudes redistribute: nearby checkpoints preserve stronger rank correspondence than distant ones, and large changes concentrate at positions that recur across text samples and transfer across evaluation domains. Controlled experiments further show that changes in the natural bypass effect cannot be reduced to a single downstream sensitivity: in replicated Pythia runs, local missing-update magnitude grows while the pooled matched downstream response decreases, whereas OLMo-2 7B exhibits a different balance. These matched responses also depend on perturbation strength and direction, without identifying targeted compensation. Together, our results show that longitudinal layer sensitivity is structured but not static, and that single-checkpoint intervention responses should be interpreted in the context of how the underlying perturbation pathway evolves during training.