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This paper first classify memorization- and generalization-heavy models using grokking in modular addition and compare their responses to unlearning, showing that the latter suffer greater retain damage, i.e., a larger performance drop on the retain set.
Masked diffusion language models revise many masked output positions in parallel and call a response answer-first when the final answer commits before the reasoning printed ahead of it, which limits how far the intervention result can be generalized.
These results reframe existing window-style samplers, previously motivated by efficiency, as the minimal fix for a reasoning pathology they were never designed to address.
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