Decodable In-Context State and Model Output Across Training

Abstract:Prior work established that a probe can decode an in-context binding on model errors and that probe-guided steering can repair some of them. We follow probe accuracy, model output, and steering response across public pretraining and post-training checkpoints. Probe accuracy rises during Pythia pretraining, while probe-guided steering moves from negligible all-trial benefit to a larger benefit at two model sizes. Saved scores distinguish probe-correct errors with low and above-uniform model probability for the correct candidate. Oracle-target steering already repairs many early errors, but saved aggregates cannot separate target quality from intervention sensitivity. A held-out comparison of decoders trained on the final state or candidate logits finds no detected final-state advantage on late-checkpoint model errors. An information-theoretic counterexample explains why decodability on errors alone cannot establish discarded output information. The connection to downstream omissions remains open.

Machine Learning (cs.LG)

arXiv:2609.31401 [cs.LG]

(or arXiv:2609.31401v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.31401 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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