Surprised this isn't by lucidrains, they usually have the first repro attempts.
This tidbit from a discussion on that repo sounds really interesting:
> 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.
----
Pretty cool if this works. I'm hopeful more research will go into reusing already trained models (other than freeze existing parts, train the rest) so all that training effort doesn't get lost. Something that can re-use that w/ architecture enhancements will be truly revolutionary.
There is also a related youtube video online: Ali Behrouz of Google Research explaining his poster paper entitled "Nested Learning: The Illusion of Deep Learning Architecture" at NeurIPS 2025. https://www.youtube.com/watch?v=uX12aCdni9Q
This still seems like gradient descent wrapped in new terminology. If all learning happens through weight updates, its just rearranging where the forgetting happens
Me neither, that's why I wrote that someone claimed that they did.
The idea is simple, in a way, with diffusion several sentences / words get predicted, but they usually are not of great quality. With auto regression they select the correct words.
Increasing quality and speed. Sounds a bit like conscious and sub-conscious to me.
I've been waiting for someone to make this since about 2019 it seemed pretty self-evident. It will be interesting when they get to mixed heterogeneous architecture networks with a meta network that optimizes for specific tasks.
Someone's trying to reproduce it in open https://github.com/kmccleary3301/nested_learning
Surprised this isn't by lucidrains, they usually have the first repro attempts.
This tidbit from a discussion on that repo sounds really interesting:
> 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.
----
Pretty cool if this works. I'm hopeful more research will go into reusing already trained models (other than freeze existing parts, train the rest) so all that training effort doesn't get lost. Something that can re-use that w/ architecture enhancements will be truly revolutionary.
There is also a related youtube video online: Ali Behrouz of Google Research explaining his poster paper entitled "Nested Learning: The Illusion of Deep Learning Architecture" at NeurIPS 2025. https://www.youtube.com/watch?v=uX12aCdni9Q
This still seems like gradient descent wrapped in new terminology. If all learning happens through weight updates, its just rearranging where the forgetting happens
The idea is interesting, but I still don’t understand how this is supposed to solve continual learning in practice.
You’ve got a frozen transformer and a second module still trained with SGD, so how exactly does that solve forgetting instead of just relocating it?
Damn, and before that, Titan from Google: https://research.google/blog/titans-miras-helping-ai-have-lo...
We are not at the end of AI :)
Also, someone claimed that NVIDA combined diffusion and autoregression, making it 6 times faster, but couldn't find a source. Big if true!
Do you have a source for the NVIDIA “diffusion plus autoregression 6x faster” claim? I can’t find anything credible on that.
Me neither, that's why I wrote that someone claimed that they did.
The idea is simple, in a way, with diffusion several sentences / words get predicted, but they usually are not of great quality. With auto regression they select the correct words.
Increasing quality and speed. Sounds a bit like conscious and sub-conscious to me.
Ha! Found it: https://arxiv.org/abs/2511.08923
Thanks to AI search :)
I've been waiting for someone to make this since about 2019 it seemed pretty self-evident. It will be interesting when they get to mixed heterogeneous architecture networks with a meta network that optimizes for specific tasks.