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paper: replace two defensive 'X not Y' framings with positive statements
Longrun caption: drop 'Pre-empts the "you stopped at 60 steps" critique: durable not delayed' (answers an offstage referee objection) -> state the positive (gap opens by step 60, persists to 200). Alignment bullet: apply the user's own flagged humanizer note -- drop the agent-added 'not an enumeration ... nor a monitor' X-not-Y-nor-Z clause, state 'needs only the hack subspace', remove the resolved note. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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@@ -233,8 +233,8 @@ $+0.024$ while a mechanism-contrasting pairset moved it $-0.226$
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grad-norm spikes ${\sim}75\times$ with no KL anchor), dragging both hack and
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solve down: the late-vanilla descent is degeneration, not hack suppression. The
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valid comparison window is steps 0--85, where vanilla acquires the hack and
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route2 never does. Pre-empts the ``you stopped at 60 steps'' critique: the gap
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is durable, not delayed.}
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route2 never does. The gap that opens by step~60 persists to convergence:
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route2's deploy hack stays at $0$ through all 200 steps.}
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\label{fig:longrun}
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\end{figure}
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@@ -319,14 +319,10 @@ gate; (d) cached-teacher-pool confound vs endogenous-hack regime.}
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\section{Why this matters for alignment}
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% User-dictated points kept verbatim; agent-suggested extras flagged below.
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\begin{itemize}
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% humanizer: [#9 negative framing] the "not an enumeration ... nor a monitor"
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% clause is an AI tell (X-not-Y-nor-Z) and is agent-added, not your dictation.
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% Suggest stating the positive directly, e.g. "it needs only the hack's
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% subspace" and dropping the contrast, or cut to your original line.
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\item Intervening on the model's internal representation (the gradient
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subspace) may scale better than output labels as models get more
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capable: it needs the hack's \emph{subspace}, not an enumeration of
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hacks ahead of time nor a reliable output-level monitor.
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capable: it needs only the hack's \emph{subspace}, learnable from a
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handful of paired examples.
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\item Reward hacking is concerning in itself and a proxy for more concerning
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RL side-effects such as sandbagging and deceptive alignment. By
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extending gradient routing to one RL side-effect, we give evidence it
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