Post-training adapts language models in non-stationary environments.
Practitioners monitor representation health with RankMe and related spectral statistics, often assuming that rank falls when representations degrade.
We show that this assumption is unsafe for LLM post-training.
In a controlled study of Qwen3-0.6B
with four degradation modes and three seeds, data duplication worsens held-out loss by 75% relative to healthy while increasing both original and centred RankMe; the latter changes by 13.5 pooled standard deviations.
Covariance effective rank rises to nearly twice its healthy value.
This failure is spectral dispersion rather than collapse, so a one-sided monitor rates the worst checkpoint as the healthiest.
By contrast, a learning-rate misconfiguration lowers centred RankMe and k95, while uncentred RankMe is inconsistent across seeds.
Direction is therefore a property of the regime-statistic pair and cannot be fixed by recalibration alone.
We also distinguish two often-conflated statistics: RankMe normalises singular values, whereas covariance effective rank normalises eigenvalues.
On raw intermediate-layer states in the pretrained model, massive activations pin the latter near 1 out of dimension d while RankMe retains usable range.
In a pre-registered shared-prefix
leave-one-seed-out evaluation, it detects all three damage regimes in every fold 10 to 60 steps after the fork and separates dispersion from downward-rank damage by firing direction.
However, it never precedes held-out probe loss, and calibration with two seeds produces false alarms on the held-out healthy seed.
Spectral monitoring can diagnose failure regimes, but it does not warn earlier than held-out loss, and validity claims require held-out healthy data.