Google releases reward-lens — a mechanistic interpretability library for reward models

Google introduced reward-lens, adapting interpretability tools (logit lens, activation patching, sparse autoencoders) for RLHF reward models — which use scalar regression heads instead of vocabulary unembeddings — closing a long-standing gap in understanding what reward models actually learn.