Would it be possible wrap a pre-trained model with the nested learning architecture

[bjoern79de]

I'm wondering if it would be possible to initialize the "longterm-memory"-layers with pre-trained weights and do a "finetuning" just for the HOPE-specific layers (inspired by https://github.com/fabienfrfr/tptt).

[kmccleary3301]

Short version: Yes. You can load a pretrained transformer backbone, freeze it, and train only the HOPE/TITAN/CMS memory pathways.

In principle, you would:

• Freeze the shared transformer spine (embeddings, attention/MLP blocks, layer norms, lm_head) and keep lm_head.weight tied to embed.weight.
• Train only the HOPE/TITAN memory modules (TITAN level, CMS levels, self-modifier projections, inner-optimizer state).
• Treat this like an adapter-style continual-learning finetune: base model provides stable representations; HOPE/CMS learn to adapt/test-time-learn on top.

Practically, the steps would probably look like this:

1. Load a base checkpoint into the transformer backbone; skip HOPE-specific keys.
2. Freeze backbone params (requires_grad=False or zero LR), but ensure weight tying remains (lm_head.weight = embed.weight).
3. Optimizer groups: backbone frozen; HOPE/CMS/TITAN param groups trainable with Muon/AdamW. Ensure that the inner-optimizer/memorize paths respect requires_grad so frozen tensors aren’t mutated.
4. Stable settings to start: shorter seq (1–2k), smaller teach_scale and inner LR, surprise gate on (low surprise_threshold), BF16 + SDPA. Warm up 1–5k steps before lengthening sequences or raising teach_scale.
5. Evaluate deltas: run baseline vs memorize (passkey/NIAH/continual) to confirm memories learn while the backbone stays fixed; log teach-signal norms and inner-update magnitudes.

A few caveats though:

• A backbone pretrained without HOPE may give suboptimal features for self-mod; expect some headroom loss vs full finetune.
• Align tokenization/positional encoding with the base checkpoint; mismatches will hurt.
• If you later unfreeze the backbone, keep weight tying intact and recheck optimizer groups.

If helpful, I can probably add a frozen_backbone: true config toggle plus a load-filter script to map a pretrained transformer checkpoint into HOPE while leaving HOPE/CMS trainable.

Obviously though, this is a reproduction of super new architecture research, so there is always a small chance of odd behavior that we won't understand until the authors publish the full version, or we better understand it with trials. However, tests so far have been pretty robust. Chances of that seem to be fairly low.