I have not looked carefully but it seems like this is over-promising on avoiding catastrophic forgetting.
The "trunk learning rate" is set at 0.1x the learning rate for the experts, so learning on different subjects disproportionately happens in the experts, and the trunk portion is comparatively more stable. But the population of experts can grow and shrink:
> The pool grows when it is short of capacity and shrinks when parts of it stop being asked for.
So:
- doesn't the trunk then _eventually_ still undergo catastrophic forgetting, it just may take much longer?
- and before that point, catastrophic forgetting happens in stepwise chunks whenever the expert pool shrinks?
Mini-AGI is a totally inappropriate name - it seems what this project is shooting for, but not delivering on, is being a language model with "continual learning".
Where it seems to fail, by design, on this goal is in delivering continual learning that is more than just "memorization with LRU catastrophic forgetting".
That said, props to the author for thinking different and actually implementing something. Maybe the project can grow into something more, or inspire different ideas, if they continue to work on it.
I'd love to explore a question. If I have a standard elementary school math textbook, along with all the results of every correct and incorrect answer my child got on the textbook exercises and class tests, is it possible to train a model that can help me figure out what concepts my child is struggling with and have that model provide help?
If you actually scroll through the transcript he links to, you will see that something that looks like it could be training is happening, but no coherent responses are coming out at any point. At least not that I saw skimming through.
That might explain why there are no benchmarks of any kind.
Nobody will throw rocks, I think most people are curious/suspicious about the big players and wants more hands-on since we suspect that this all will come down in cost soon enough.
Seems interesting, I've been messing with a lot of continuous learning approaches lately and it's cool to see something that's built from the ground up for avoiding catastrophic forgetting. Worth a clone for sure
Is this architecture actually able to generalize or is it mostly based on memorization? Have you tried some basic tasks that require generalization? e.g. number addition etc?
Interesting. I wonder how much could be gained from using tokenization, which makes the model work at a semantic level rather than a syntactic level? I think it’s a force multiplier, but idk if it works here.
This is an interesting approach. First of all, thanks for sharing your work. I've done. I want to say similar work in that I have trained continuous learning models and I have also offloaded parametric knowledge to hard drive people underestimate how difficult that is to do in a functional model. I look forward to digging in deeper.
This is pretty cool, thanks for sharing. Whe I read it first and saw "continual" I thought for a minute that it was implementing an idea I've been thinking about:
I want to have an agent that thinks continually/non-stop. Imagine a loop of "train of thought" that goes into the LLM and then out. Keep it going so that it "rumiates" thr way we do.
Then, add some sort of "messages" or IRQs when I want to communicate with it. To ask it things and whatnot. I think that sort of cycle in addition to this learning you are doing is what is missing for real AGI.
I love the approach of this but "It has to not forget. A model that learns continually and overwrites itself is worse than one that does not learn at all."
Is highly misguided.
While the platonic ideal of Lt Commander Data is appealing, The parable of funes the memorious (Jorge Luis Borges) comes to mind.
I wish I could understand what a single graph in that nice graphic of graphs meant. No explanation for any vertical or horizontal axis. Looks pretty though.
What's the advantage of doing this, versus becoming good at context management and RAG? I always found trained knowledge unreliable, given that it is lossy by construction.
Have you thought about making the whole thing "self-similar"? Every time I hear about MoE I think (and I know it's way easier thought than done) "why stay shallow"?
I mean by that: would it be possible to extend/adapt the architecture so that an expert can be a previously trained Mini-AGI model? And recurse like this? Inuitively I would think some form of generalization could happen, as higher level experts (in the recurrence stack) would become sort of the "intuition" layer.
The expert swapping architecture is very nice. Have you considered doing nested reinforcement learning where you use the nesting as a sort of low pass filter?
The concept is as follows: You train a critic to mimic the datastream and then you train against the critic instead of training against the data. The idea behind this is that the critic will memorize the training data so you do not need to store the full training data anymore. One of the biggest issues with current online stochastic gradient descent is that it is inherently a memory-less technique where the training data acts as the memory.
You can spin this further by going deeper with the nesting and then dropping the supervised critic. I forgot how to put it in words but the goal is that by having a model train against a critic of the critic, you can then drop the top level critic and instead use the mid level critic itself as your meta learning objective to train the actor against an unlabeled data stream.
Top level critic: learns to mimic the labeled training data via online SGD, then you add a simple hand written loss function to compare the predicted output with a given input. Basically you build a model specifically for distillation.
Mid level critic: learns a reward function that mimics the top level critic directly but only gets to see the unlabeled training data and the result of the top level critic.
Actor: The actor is exclusively trained against the mid level critic
Through this concept you end up with the existing training data stored as objective inside the mid level critic so you end up training not only against the latest data but also the already memorized data which should lower catastrophic forgetting. Of course at some point you might need to update the mid level critic again and to avoid that you might get away with just adding a very very wide Linear RNN / State Space Model / Mamba / Gated Delta Net as the middle critic (shower thought: use internal RNN states to represent LoRA vectors).
This is slop. 8M parameter dense model with context length 64 that you train on enwik9 in 2h will have 1.15 bpb. This model has 1.8 (bits per byte, lower is better).
What motivated you decide to release this. OpenAI or Anthropic will just hoover it up, maybe scale it up and use it if they are interested.
You probably won't know if they do, and the chance they will give you something back is near zero. Why did you release rather than try to scale and build yourself?
(I've been working on some thing, not similar, but not dissimilar in goal - and I just can't get over the fact that tech will steal without giving back)
Show HN: Mini-AGI – Dynamic continual learning model trained on 8GB VRAM
(github.com)263 points by volotat 21 September 2026 | 60 comments
Comments
The "trunk learning rate" is set at 0.1x the learning rate for the experts, so learning on different subjects disproportionately happens in the experts, and the trunk portion is comparatively more stable. But the population of experts can grow and shrink:
> The pool grows when it is short of capacity and shrinks when parts of it stop being asked for.
So:
- doesn't the trunk then _eventually_ still undergo catastrophic forgetting, it just may take much longer?
- and before that point, catastrophic forgetting happens in stepwise chunks whenever the expert pool shrinks?
Where it seems to fail, by design, on this goal is in delivering continual learning that is more than just "memorization with LRU catastrophic forgetting".
That said, props to the author for thinking different and actually implementing something. Maybe the project can grow into something more, or inspire different ideas, if they continue to work on it.
That might explain why there are no benchmarks of any kind.
I want to have an agent that thinks continually/non-stop. Imagine a loop of "train of thought" that goes into the LLM and then out. Keep it going so that it "rumiates" thr way we do.
Then, add some sort of "messages" or IRQs when I want to communicate with it. To ask it things and whatnot. I think that sort of cycle in addition to this learning you are doing is what is missing for real AGI.
Is highly misguided.
While the platonic ideal of Lt Commander Data is appealing, The parable of funes the memorious (Jorge Luis Borges) comes to mind.
The concept is as follows: You train a critic to mimic the datastream and then you train against the critic instead of training against the data. The idea behind this is that the critic will memorize the training data so you do not need to store the full training data anymore. One of the biggest issues with current online stochastic gradient descent is that it is inherently a memory-less technique where the training data acts as the memory.
You can spin this further by going deeper with the nesting and then dropping the supervised critic. I forgot how to put it in words but the goal is that by having a model train against a critic of the critic, you can then drop the top level critic and instead use the mid level critic itself as your meta learning objective to train the actor against an unlabeled data stream.
Top level critic: learns to mimic the labeled training data via online SGD, then you add a simple hand written loss function to compare the predicted output with a given input. Basically you build a model specifically for distillation. Mid level critic: learns a reward function that mimics the top level critic directly but only gets to see the unlabeled training data and the result of the top level critic. Actor: The actor is exclusively trained against the mid level critic
Through this concept you end up with the existing training data stored as objective inside the mid level critic so you end up training not only against the latest data but also the already memorized data which should lower catastrophic forgetting. Of course at some point you might need to update the mid level critic again and to avoid that you might get away with just adding a very very wide Linear RNN / State Space Model / Mamba / Gated Delta Net as the middle critic (shower thought: use internal RNN states to represent LoRA vectors).
What motivated you decide to release this. OpenAI or Anthropic will just hoover it up, maybe scale it up and use it if they are interested.
You probably won't know if they do, and the chance they will give you something back is near zero. Why did you release rather than try to scale and build yourself?
(I've been working on some thing, not similar, but not dissimilar in goal - and I just can't get over the fact that tech will steal without giving back)