Thomas Fel

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.

Optimizing concept alignment is a multi-objective challenge, and surprisingly, just 0.1% of paired data can yield strong instance-level alignment when done right.

Steering neural networks through the intrinsic geometry of their activations unlocks more natural and controllable behaviors than traditional linear interventions.

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.