This looks absolutely fantastic, please accept my meagre professional jealousy. I have long bemoaned manual hyperparam fiddling . I have on occasion dabbled with nonparametric ("genetic") methods of hyperparam tuning inspired by AutoML... but then you still have to manually tune the evolutionary hyperparams.
Finding a way to derive this from the gradients is amazing.
This is definitely not just another machine learning method. It comes from a complete cognitive science theory, rooted in a complete understanding of intelligence and consciousness.
1. an overly strong surprise is like PTSD in humans - it changes the model's previously learned experience forever, this is what we want to avoid
2. it's bound to happen, and our PILR-S is designed to keep the learning rate within the bell curve and decreasing as the surprise decreases (less new information, less learning).
But doesn’t this lead to the opposite problem: creating a model that can never learn to let go of an early-life mental model picked up from a skewed dataset?
By analogy to humans: if this model were raised in a cult, and then let out into the real world, it would be seemingly incapable of unlearning the cult’s indoctrination, despite the real-world data all contradicting it — as all of this real-world data would be too surprising for the model to accept.
Or, for a maybe-more-likely situation you might encounter in e.g. incremental model re-training of old models for chronologically-newer info: a model trained this way would “stubbornly” refuse to accept any major shift in scientific consensus on a topic.
The human cognitive architecture seems to solve this problem by 1. buffering this rejected-for-being-too-out-there info in a way where it can at least be pattern-recognized; and then 2. noticing when a lot of different, seemingly independent, seemingly trustworthy sources begin matching on the rejected pattern. At that point, the human brain seems to swing the other way — experiencing a “crisis of faith” per se.
That's a brilliant and crucial point. You've pinpointed the central dialectic of this architecture: the trade-off between stability (resisting catastrophic forgetting) and plasticity (updating core beliefs).
You are absolutely right that a poorly configured model could become "dogmatic," incapable of escaping an early "cult" indoctrination. This cognitive rigidity, however, is not a hardcoded flaw but a tunable personality trait .
This is where the remaining hyperparameters come into play. We still define:
1. The initial `learning_rate`, setting its baseline openness.
2. The `sigma_threshold` for the surprise EMA, which defines its "trust window." (This can be adjusted at any time! It does not affect any past training progression. For generative models, such as LLMs, you can even try to let them specify themselves)
A narrow sigma creates a conservative, "skeptical" model, while a wider sigma creates a more "open-minded" one that is more willing to entertain paradigm shifts. So, the paradigm shift is this: we are no longer micromanaging how the model learns moment-to-moment. Instead, we are defining its cognitive temperament or learning style. Your "crisis of faith" mechanism is the logical next step—a meta-learning process we are actively exploring. Thank you for the incredibly sharp insight.
Finding a way to derive this from the gradients is amazing.
https://github.com/dmf-archive/IPWT
also produced an interesting science fiction world.(really just like that? RE knows...)
:)
- Surprise is detected by the norm of the gradients. So, doesn’t this suggest that the model already has a way of adjusting to surprise?
- Is there a danger of model instability when the gradients become larger and the learning rate is also increased?
2. it's bound to happen, and our PILR-S is designed to keep the learning rate within the bell curve and decreasing as the surprise decreases (less new information, less learning).
By analogy to humans: if this model were raised in a cult, and then let out into the real world, it would be seemingly incapable of unlearning the cult’s indoctrination, despite the real-world data all contradicting it — as all of this real-world data would be too surprising for the model to accept.
Or, for a maybe-more-likely situation you might encounter in e.g. incremental model re-training of old models for chronologically-newer info: a model trained this way would “stubbornly” refuse to accept any major shift in scientific consensus on a topic.
The human cognitive architecture seems to solve this problem by 1. buffering this rejected-for-being-too-out-there info in a way where it can at least be pattern-recognized; and then 2. noticing when a lot of different, seemingly independent, seemingly trustworthy sources begin matching on the rejected pattern. At that point, the human brain seems to swing the other way — experiencing a “crisis of faith” per se.
You are absolutely right that a poorly configured model could become "dogmatic," incapable of escaping an early "cult" indoctrination. This cognitive rigidity, however, is not a hardcoded flaw but a tunable personality trait .
This is where the remaining hyperparameters come into play. We still define:
1. The initial `learning_rate`, setting its baseline openness.
2. The `sigma_threshold` for the surprise EMA, which defines its "trust window." (This can be adjusted at any time! It does not affect any past training progression. For generative models, such as LLMs, you can even try to let them specify themselves)
A narrow sigma creates a conservative, "skeptical" model, while a wider sigma creates a more "open-minded" one that is more willing to entertain paradigm shifts. So, the paradigm shift is this: we are no longer micromanaging how the model learns moment-to-moment. Instead, we are defining its cognitive temperament or learning style. Your "crisis of faith" mechanism is the logical next step—a meta-learning process we are actively exploring. Thank you for the incredibly sharp insight.