Great work! I have been working on something very similar and will publish my results here some time next week, but can already give a sneak-peak:
The SAEs here were only trained for 100M tokens (1/3 the TinyStories[11:1] dataset). The language model was trained for 3 epochs on the 300M token TinyStories dataset. It would be good to validate these results with more 'real' language models and train SAEs with much more data.
I can confirm that on Gemma-2-2B Matryoshka SAEs dramatically improve the absorption score on the first-letter task from Chanin et al. as implemented in SAEBench!
Is there a nice way to extend the Matryoshka method to top-k SAEs?
Yes! My experiments with Matryoshka SAEs are using BatchTopK.
Are you planning to continue this line of research? If so, I would be interested to collaborate (or otherwise at least coordinate on not doing duplicate work).
Great work! I have been working on something very similar and will publish my results here some time next week, but can already give a sneak-peak:
I can confirm that on Gemma-2-2B Matryoshka SAEs dramatically improve the absorption score on the first-letter task from Chanin et al. as implemented in SAEBench!
Yes! My experiments with Matryoshka SAEs are using BatchTopK.
Are you planning to continue this line of research? If so, I would be interested to collaborate (or otherwise at least coordinate on not doing duplicate work).