Seonglae ChoThe architecture has separate for each layer while sharing only the latent dictionary for scaling, where : source layer-specific encoder, : target layer-specific decoder, : reconstructed layer activation from source latent.
Each input-output layer pair has its own encoder-decoder weight pair. Unlike cross-layer transcoders (Circuit Tracing), encoders are not shared; instead, layers share the same latent space, achieved through loss-based approximation.
This is solved through alignment via co-training, where an alignment loss is added during training to force the latents to match, causing them to converge to a shared latent space.
The original purpose is cross-layer mapping and feature alignment, but it can be used for various purposes such as model diffing and scaling transfer
Decoder sparsity loss
Crosscoders
CrossCoder (2024)
with Cross fine-tuning model & scaling transferability by diffing within same architecture
Sparse Crosscoders for Cross-Layer Features and Model Diffing
This note will cover some theoretical examples motivating crosscoders, and then present preliminary experiments applying them to cross-layer superposition and model diffing. We also briefly discuss the theory of how crosscoders might simplify circuit analysis, but leave results on this for a future update.
https://transformer-circuits.pub/2024/crosscoders/index.html
Open Source Replication of Anthropic’s Crosscoder paper for model-diffing — LessWrong
Intro Anthropic recently released an exciting mini-paper on crosscoders (Lindsey et al.). In this post, we open source a model-diffing crosscoder tra…
https://www.lesswrong.com/posts/srt6JXsRMtmqAJavD/open-source-replication-of-anthropic-s-crosscoder-paper-for
BatchTopK crosscoder to prevent Complete Shrinkage and Latent Decoupling for chat model
