3 papers
cs.LG2025
Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
Viacheslav Sinii, Nikita Balagansky, Gleb Gerasimov +6
The mechanisms by which reasoning training reshapes LLMs' internal computations remain unclear. We study lightweight steering vectors inserted into the base model's residual stream…
cs.LG2025
Teach Old SAEs New Domain Tricks with Boosting
Nikita Koriagin, Yaroslav Aksenov, Daniil Laptev +3
Sparse Autoencoders have emerged as powerful tools for interpreting the internal representations of Large Language Models, yet they often fail to capture domain-specific features n…
cs.LG2025
Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy
Nikita Balagansky, Yaroslav Aksenov, Daniil Laptev +4
Sparse Autoencoders (SAEs) have proven to be powerful tools for interpreting neural networks by decomposing hidden representations into disentangled, interpretable features via spa…