Siddharth Boppana

Transforming post-training from opaque reward optimization into a transparent process of auditing and sculpting the learning signal could revolutionize how we guide model behavior.

Sparse autoencoders, despite their popularity for extracting interpretable features, often fail to capture the underlying manifold structure of concepts, instead fragmenting them across multiple, diluted features.

LLMs often know the answer long before their "reasoning" suggests, wasting tokens on performative chain-of-thought.