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<title>faeval.git/experiments/freeze_with_decay.py, branch master</title>
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<entry>
<title>Add Phase 10A.8: freeze-with-decay confirms stale aux is main freeze failure cause;</title>
<updated>2026-03-27T21:39:17+00:00</updated>
<author>
<name>YurenHao0426</name>
<email>Blackhao0426@gmail.com</email>
</author>
<published>2026-03-27T21:39:17+00:00</published>
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alpha sweep shows perlayer_vector at alpha=0.75 matches full network

10A.8A: freeze_decay_to_000 recovers to 28.5% (vs 14.6% fixed freeze) — stale
high-weight aux is the primary cause of freeze crashes. But 28.5% &lt; DFA 31.2%
confirms continuous trainability adds ~2.7% independent value.

10A.8B: Both perlayer_vector and random_trainable optimal at alpha=0.75.
perlayer_vector +1.1% vs random_trainable +0.8% — per-layer vector is
the minimal sufficient scaffold, no network needed.

Co-Authored-By: Claude Opus 4.6 (1M context) &lt;noreply@anthropic.com&gt;
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<pre>
alpha sweep shows perlayer_vector at alpha=0.75 matches full network

10A.8A: freeze_decay_to_000 recovers to 28.5% (vs 14.6% fixed freeze) — stale
high-weight aux is the primary cause of freeze crashes. But 28.5% &lt; DFA 31.2%
confirms continuous trainability adds ~2.7% independent value.

10A.8B: Both perlayer_vector and random_trainable optimal at alpha=0.75.
perlayer_vector +1.1% vs random_trainable +0.8% — per-layer vector is
the minimal sufficient scaffold, no network needed.

Co-Authored-By: Claude Opus 4.6 (1M context) &lt;noreply@anthropic.com&gt;
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