A Bunch of Matryoshka SAEs

post by chanind, TomasD (tomas-dulka), Adrià Garriga-alonso (rhaps0dy) · 2025-04-04T14:53:56.805Z · LW · GW · 0 comments

Contents

    I don't care about any of that, just give me the SAEs!
    Neuronpedia
  Training info
    Snap loss
    Balancing absorption and hedging
  SAEs and stats
    Gemma-2-2b
      Snap Matryoshka SAEs
      Standard Matryoshka SAEs
    Gemma-2-9b
    Gemma-3-1b
None
No comments

This work was done as part of MATS 7.0.

MATS provides a generous compute stipend, and towards the end of the program we found we had some unspent compute. To let this not go to waste, we trained batch topk Matryoshka SAEs on all residual stream layers of Gemma-2-2b, Gemma-2-9b, and Gemma-3-1b, and are now releasing them publicly. The hyperparams for these SAEs were not aggressively optimized, but they should hopefully be decent. Below we describe our rationale for how these SAEs were trained and why, and the stats for each SAE. Key decisions:

• We use more narrow inner widths than in the original Matryoshka SAEs work, and increase each width by a larger amount. We do this to make it easier to study the highest-frequency features of the model.
• We include standard and snap loss variants for Gemma-2-2b. Snap loss and the rationale behind it is described in our Feature Hedging post [LW · GW]. There is probably not much practical difference between the snap and standard versions of the SAEs.
• We do not stop gradients between Matryoshka layers. We find in toy models that hedging [LW · GW] and absorption [LW · GW] pull the encoder in opposite directions, and this helps moderate the severity of feature hedging in Matryoshka SAE inner layers.

I don't care about any of that, just give me the SAEs!

You can load all of the SAEs using SAELens via the following releases:

• Gemma-2-2b snap loss matryoshka: gemma-2-2b-res-snap-matryoshka-dc
• Gemma-2-2b standard matryoshka: gemma-2-2b-res-matryoshka-dc
• Gemma-2-9b standard matryoshka: gemma-2-9b-res-matryoshka-dc
• Gemma-3-1b standard matroshka: gemma-3-1b-res-matryoshka-dc

For each release, the SAE ID is just the corresponding Transformer Lens post residual stream hook point, e.g. blocks.5.hook_resid_post for the layer 5 residual stream SAE.

Each SAE can be loaded in SAELens as follows:

from sae_lens import SAE

sae = SAE.from_pretrained("<release>", "<sae_id>")[0]

For instance, to load the layer 5 snap variant SAE for gemma-2-2b, this would look like the following:

sae = SAE.from_pretrained("gemma-2-2b-res-snap-matryoshka-dc", "blocks.5.hook_resid_post")[0]

Neuronpedia

Neuronpedia has generously hosted some of these SAEs, with more coming soon. Check them out at: https://www.neuronpedia.org/res-matryoshka-dc.

Matryoshka SAEs should be much better than standard SAEs at finding general, high-frequency concepts like parts of speech. In standard SAEs, latents tracking these concepts will get shot to pieces by feature absorption as they co-occur with so many other concepts. As Matryoshka SAEs should be much more resilient to absorption, we thus expect to find more meaningful high-density latents in Matryoshka SAEs (although these latents may be messed up by feature hedging instead). For instance, here's a high-density first-layer latent from layer 12 of Gemma-2-2b, which appears to (very noisily) perform a grammatical function similar to Treebank's IN (Preposition or subordinating conjunction) part of speech. 

Higher frequency concepts should be concentrated in earlier latent indices. The highest frequency concepts should be in latents 0-127, then the next highest frequency should be in latents 128-511, etc...

Training info

All Matryoshka SAEs in this release are trained on 750M tokens from the Pile using a modified version of SAELens. The SAEs are all 32k width with the following Matryoshka levels: 128, 512, 2048, 8192, and 32768. We included two layers (128, 512) that are much narrower than the model residual stream to make it easier to study what the first  features are that the SAE learns. These are all batch top-k SAEs, following the original Matryoshka SAEs work. We largely did not optimize hyperparams for these SAEs, so it's likely possible to squeeze out more performance from the SAE with optimized choices of learning rate and more training tokens, but hopefully these SAEs should be decent.

Snap loss

One of the notable components of this release is the addition of snap loss variants of all SAEs for Gemma-2-2b. Snap loss is described in our post on Feature Hedging [LW · GW], and involves switching the reconstruction loss of the SAE from MSE to L2 mid-way through training. Practically, we don't see much difference in SAEs trained on LLMs using snap loss, but are releasing these regardless in case others are curious to investigate the effect of snap loss, as we have the SAE trained anyway. If you notice a meaningful difference in practice between the snap loss and standard variants of these SAEs, please let us know!

Balancing absorption and hedging

Intuitively, it might seem like we'd want the inner layers of Matryoshka SAEs to be insulated from gradients from outer layers. Outer layers will pull the inner latents towards absorption, which defeats the purpose of a Matryoshka SAE! However, in toy models, hedging and absorption have opposite effects on the SAE encoder, so allowing some absorption pressure can help counteract the hedging of the SAE and improve performance. We notice that the dictionary_learning implementation of matryoshka SAEs also does not stop gradients between layers, and likely this is because stopping gradients causes hedging to mess up the SAE more severly.

For a further investigation of balancing hedging and absorption in Matryoshka SAEs, check out this colab.

We suspect that it may be possible to intentionally balance hedging with absorption in a more optimal way, and we plan to investigate this in future work. 

SAEs and stats

Below we list all the SAEs trained along with some core stats.

Gemma-2-2b

We trained both snap and standard variants of SAEs for Gemma-2-2b. These SAEs have the relase ID gemma-2-2b-res-snap-matryoshka-dc for snap-loss variant, and gemma-2-2b-res-matryoshka-dc for the standard variant.

Snap Matryoshka SAEs

layer	SAE ID	width	l0	explained variance
0	blocks.0.hook_resid_post	32768	40	0.919964
1	blocks.1.hook_resid_post	32768	40	0.863969
2	blocks.2.hook_resid_post	32768	40	0.858767
3	blocks.3.hook_resid_post	32768	40	0.815844
4	blocks.4.hook_resid_post	32768	40	0.821094
5	blocks.5.hook_resid_post	32768	40	0.797083
6	blocks.6.hook_resid_post	32768	40	0.79815
7	blocks.7.hook_resid_post	32768	40	0.78946
8	blocks.8.hook_resid_post	32768	40	0.779236
9	blocks.9.hook_resid_post	32768	40	0.759022
10	blocks.10.hook_resid_post	32768	40	0.743998
11	blocks.11.hook_resid_post	32768	40	0.731758
12	blocks.12.hook_resid_post	32768	40	0.725974
13	blocks.13.hook_resid_post	32768	40	0.727936
14	blocks.14.hook_resid_post	32768	40	0.727065
15	blocks.15.hook_resid_post	32768	40	0.757408
16	blocks.16.hook_resid_post	32768	40	0.751874
17	blocks.17.hook_resid_post	32768	40	0.763654
18	blocks.18.hook_resid_post	32768	40	0.77644
19	blocks.19.hook_resid_post	32768	40	0.768622
20	blocks.20.hook_resid_post	32768	40	0.761658
21	blocks.21.hook_resid_post	32768	40	0.765593
22	blocks.22.hook_resid_post	32768	40	0.741098
23	blocks.23.hook_resid_post	32768	40	0.729718
24	blocks.24.hook_resid_post	32768	40	0.754838

Standard Matryoshka SAEs

layer	SAE ID	width	l0	explained variance
0	blocks.0.hook_resid_post	32768	40	0.91832
1	blocks.1.hook_resid_post	32768	40	0.863454
2	blocks.2.hook_resid_post	32768	40	0.841324
3	blocks.3.hook_resid_post	32768	40	0.814794
4	blocks.4.hook_resid_post	32768	40	0.820418
5	blocks.5.hook_resid_post	32768	40	0.796252
6	blocks.6.hook_resid_post	32768	40	0.797322
7	blocks.7.hook_resid_post	32768	40	0.787601
8	blocks.8.hook_resid_post	32768	40	0.779433
9	blocks.9.hook_resid_post	32768	40	0.75697
10	blocks.10.hook_resid_post	32768	40	0.745011
11	blocks.11.hook_resid_post	32768	40	0.732177
12	blocks.12.hook_resid_post	32768	40	0.726209
13	blocks.13.hook_resid_post	32768	40	0.719405
14	blocks.14.hook_resid_post	32768	40	0.719056
15	blocks.15.hook_resid_post	32768	40	0.756888
16	blocks.16.hook_resid_post	32768	40	0.742889
17	blocks.17.hook_resid_post	32768	40	0.757294
18	blocks.18.hook_resid_post	32768	40	0.76921
19	blocks.19.hook_resid_post	32768	40	0.766661
20	blocks.20.hook_resid_post	32768	40	0.760939
21	blocks.21.hook_resid_post	32768	40	0.759883
22	blocks.22.hook_resid_post	32768	40	0.740612
23	blocks.23.hook_resid_post	32768	40	0.729678
24	blocks.24.hook_resid_post	32768	40	0.747313

Gemma-2-9b

These SAEs have the release ID gemma-2-9b-res-matryoshka-dc.

layer	path	width	l0	explained variance
0	blocks.0.hook_resid_post	32768	60	0.942129
1	blocks.1.hook_resid_post	32768	60	0.900656
2	blocks.2.hook_resid_post	32768	60	0.869154
3	blocks.3.hook_resid_post	32768	60	0.84077
4	blocks.4.hook_resid_post	32768	60	0.816605
5	blocks.5.hook_resid_post	32768	60	0.826656
6	blocks.6.hook_resid_post	32768	60	0.798281
7	blocks.7.hook_resid_post	32768	60	0.796018
8	blocks.8.hook_resid_post	32768	60	0.790385
9	blocks.9.hook_resid_post	32768	60	0.775052
10	blocks.10.hook_resid_post	32768	60	0.756327
12	blocks.12.hook_resid_post	32768	60	0.718319
13	blocks.13.hook_resid_post	32768	60	0.714065
14	blocks.14.hook_resid_post	32768	60	0.709635
15	blocks.15.hook_resid_post	32768	60	0.706622
16	blocks.16.hook_resid_post	32768	60	0.687879
17	blocks.17.hook_resid_post	32768	60	0.695821
18	blocks.18.hook_resid_post	32768	60	0.691723
19	blocks.19.hook_resid_post	32768	60	0.690914
20	blocks.20.hook_resid_post	32768	60	0.684599
21	blocks.21.hook_resid_post	32768	60	0.691355
22	blocks.22.hook_resid_post	32768	60	0.705531
23	blocks.23.hook_resid_post	32768	60	0.702293
24	blocks.24.hook_resid_post	32768	60	0.707655
25	blocks.25.hook_resid_post	32768	60	0.721022
26	blocks.26.hook_resid_post	32768	60	0.721717
27	blocks.27.hook_resid_post	32768	60	0.745809
28	blocks.28.hook_resid_post	32768	60	0.753267
29	blocks.29.hook_resid_post	32768	60	0.76466
30	blocks.30.hook_resid_post	32768	60	0.763025
31	blocks.31.hook_resid_post	32768	60	0.765932
32	blocks.32.hook_resid_post	32768	60	0.760822
33	blocks.33.hook_resid_post	32768	60	0.73323
34	blocks.34.hook_resid_post	32768	60	0.746912
35	blocks.35.hook_resid_post	32768	60	0.738031
36	blocks.36.hook_resid_post	32768	60	0.730805
37	blocks.37.hook_resid_post	32768	60	0.722875
38	blocks.38.hook_resid_post	32768	60	0.715494
39	blocks.39.hook_resid_post	32768	60	0.7044
40	blocks.40.hook_resid_post	32768	60	0.711277

Gemma-3-1b

These SAEs have the release ID gemma-3-1b-res-matryoshka-dc.

layer	SAE ID	width	l0	explained variance
0	blocks.0.hook_resid_post	32768	40	0.99118
1	blocks.1.hook_resid_post	32768	40	0.985819
2	blocks.2.hook_resid_post	32768	40	0.981468
3	blocks.3.hook_resid_post	32768	40	0.979252
4	blocks.4.hook_resid_post	32768	40	0.973719
5	blocks.5.hook_resid_post	32768	40	0.977229
6	blocks.6.hook_resid_post	32768	40	0.982247
7	blocks.7.hook_resid_post	32768	40	0.989271
8	blocks.8.hook_resid_post	32768	40	0.985447
9	blocks.9.hook_resid_post	32768	40	0.985869
10	blocks.10.hook_resid_post	32768	40	0.98235
11	blocks.11.hook_resid_post	32768	40	0.980853
12	blocks.12.hook_resid_post	32768	40	0.977682
13	blocks.13.hook_resid_post	32768	40	0.969005
14	blocks.14.hook_resid_post	32768	40	0.956484
15	blocks.15.hook_resid_post	32768	40	0.937399
16	blocks.16.hook_resid_post	32768	40	0.928849
17	blocks.17.hook_resid_post	32768	40	0.912209
18	blocks.18.hook_resid_post	32768	40	0.904198
19	blocks.19.hook_resid_post	32768	40	0.895405
20	blocks.20.hook_resid_post	32768	40	0.883044
21	blocks.21.hook_resid_post	32768	40	0.868396
22	blocks.22.hook_resid_post	32768	40	0.831975
23	blocks.23.hook_resid_post	32768	40	0.793732
24	blocks.24.hook_resid_post	32768	40	0.7452

0 comments

Comments sorted by top scores.