Genie: Generative Interactive Environments Bruce et al.
How is that paper alignment-relevant?
neverix
Karma: 105
Extracting SAE task features for in-context learning
Self-explaining SAE features
Freshman’s dream sparsity loss
A similar regularizer is known as Hoyer-Square.
Pick a value for and a small . Then define the activation function in the following way. Given a vector , let be the value of the th-largest entry in . Then define the vector by
Is in the following formula a typo?
To clarify, I thought it was about superposition happening inside the projection afterwards.
This happens in transformer MLP layers. Note that the hidden dimen
Is the point that transformer MLPs blow up the hidden dimension in the middle?
Activation additions in generative models
Also related is https://arxiv.org/abs/2210.10960. They use a small neural network to generate steering vectors for the UNet bottleneck in diffusion to edit images using CLIP.
From a conversation on Discord:
Do you have in mind a way to weigh sequential learning into the actual prior?
Dmitry:
good question! We haven’t thought about an explicit complexity measure that would give this prior, but a very loose approximation that we’ve been keeping in the back of our minds could be a Turing machine/Boolean circuit version of the “BIMT” weight penalty from this paper https://arxiv.org/abs/2305.08746 (which they show encourages modularity at least in toy models)
Response:
Hmm, BIMT seems to only be about intra-layer locality. It would certainly encourage learning an ensemble of features, but I’m not sure if it would capture the interesting bit, which I think is the fact that features are built up sequentially from earlier to later layers and changes are only accepted if they improve local loss.
I’m thinking about something like an existence of a relatively smooth scaling law (?) as the criterion.
So, just some smoothness constraint that would basically integrate over paths SGD could take.
We can idealize the outer alignment solution as a logical inductor.
Why outer?
You could literally go through some giant corpus with an LLM and see which samples have gradients similar to those from training on a spelling task.
There are also somewhat principled reasons for using a “fuzzy ellipsoid”, which I won’t explain here.
If you view as 2x learning rate, the ellipsoid contains parameters which will jump straight into the basin under the quadratic approximation, and we assume for points outside the basin the approximation breaks entirely. If you account for gradient noise
in the form of a Gaussian with sigma equal to gradient, the PDF of the resulting point at the basin is equal to the probability a Gaussian parametrized by the ellipsoid at the preceding point.This is wrong, but there is an interpretation of the noise as a Gaussian with variance increasing away from the basin origin.
Seems like quoting doesn’t work for LaTeX, it was definitions 2⁄3. Reading again I saw D2 was indeed applicable to sets.
A0>A1
How is orbit comparison for sets defined?
course
coarse?
This is the whole point of goal misgeneralization. They have experiments (albeit on toy environments that can be explained by the network finding the wrong algorithm), so I’d say quite plausible.
sidereal
typo?
Is RLHF updating abstract circuits an established fact? Why would it suffer from mode collapse in that case?
It is based on this. I changed it to optimize using softmax instead of straight-through estimation and added regularization for the embedded tokens.
Notebook link—this is a version that mimics this post instead of optimizing a single neuron as in the original.
EDIT: github link
This project tried this.
We use our own judgement as a (potentially very inaccurate) proxy for accuracy as an explanation and let readers look on their own at the feature dashboard interface. We judge using a random sample of examples at different levels of activation. We had an automatic interpretation scoring pipeline that used Llama 3 70B, but we did not use it because (IIRC) it was too slow to run with multiple explanations per feature. Perhaps it is now practical to use a method like this.
That is a pattern that happens frequently, but we’re not confident enough to propose any particular form. It is sometimes thrown off by random spikes, self-similarity gradually rising at larger scales, or entropy peaking in the beginning. Because of this, there is still a lot of room for improvement in cases where a human (or maybe a peak-finding algorithm) could do better than our linear metric.