fix: attribute muon-25bt larger std to Muon characteristic, not training duration alone

The muon-25bt (Muon, 25B) vs kami/norm (AdamW, 15B) comparison confounds
optimizer choice with training duration. Reframe as "Muon produces larger
post-convergence weight variance" rather than "training duration drives
variance growth."

main.tex

@@ -520,21 +520,18 @@ are visible in the per-component weight std
 (Table~\ref{tab:weight_std}, Appendix~\ref{app:weight_std}):
 
 \begin{itemize}[nosep]
-  \item \textbf{Training duration drives variance growth}: the
-        \texttt{muon-25bt} checkpoint exhibits the largest weight std
-        ($\sim$0.024 for attention projections), exceeding both 15B
-        checkpoints ($\sim$0.015--0.021), reflecting continued weight
-        drift from $\sigma_0 = 0.02$ as training progresses.
+  \item \textbf{Muon produces larger post-convergence weight
+        variance}: the \texttt{muon-25bt} checkpoint (Muon, 25B tokens)
+        exhibits the largest weight std ($\sim$0.024), exceeding both
+        15B AdamW checkpoints ($\sim$0.015--0.021), consistent with
+        Muon allowing wider parameter distributions after convergence.
   \item \textbf{Residual scaling constrains early-stage drift}: at
         equal training budget (15B tokens) and with the same AdamW
         optimizer, the GPT-2-scaled \texttt{kami-15bt} shows
         \emph{smaller} std ($\sim$0.015) than the Normal-init
         \texttt{norm-15bt} ($\sim$0.021), indicating that residual
         scaling itself limits weight drift beyond its initialization
-        effect. The Muon-trained \texttt{muon-25bt} (25B tokens)
-        exhibits the largest std because cumulative training steps
-        eventually overtake both initialization and per-step optimizer
-        constraints.
+        effect.
 \end{itemize}
 
 Critically, the residual-scaled projections maintain consistently
@@ -847,9 +844,10 @@ distribution analysis in Section~\ref{sec:num-stability}.
 
 \begin{table}[H]
 \centering
-\caption{Weight std by component across checkpoints. Non-scaled weights broaden with training duration; residual scaling
-constrains drift at equal token count (15B) but is overtaken by
-longer training (25B).
+\caption{Weight std by component across checkpoints. Non-scaled weights
+broaden with training; residual scaling constrains drift at equal
+token count (15B). Muon produces larger post-convergence weight
+variance than AdamW.
 Residual-scaled projections ($\mathbf{W}_o$, $\mathbf{W}_{\text{down}}$)
 remain bounded.}
 \label{tab:weight_std}