A helpful mental model distinguishes parameter-space edits from activation-space edits. Fine-tuning with LoRA alters model weights (the "pipes"), while activation steering modifies the information flowing through them (the "water"), clarifying two distinct approaches to model control.

Contrary to fears that reinforcement learning would push models' internal reasoning (chain-of-thought) into an unexplainable shorthand, OpenAI has not seen significant evidence of this "neural ease." Models still predominantly use plain English for their internal monologue, a pleasantly surprising empirical finding that preserves a crucial method for safety research and interpretability.

The field is moving beyond labeling concepts with sparse autoencoders. The new frontier is understanding the intricate geometric structures (manifolds) these concepts form in a model's latent space and how circuits transform them, providing a more unified, dynamic view.

Research suggests a formal equivalence between modifying a model's internal activations (steering) and providing prompt examples (in-context learning). This framework could potentially create a formula to convert between the two techniques, even for complex behaviors like jailbreaks.

A novel safety technique, 'machine unlearning,' goes beyond simple refusal prompts by training a model to actively 'forget' or suppress knowledge on illicit topics. When encountering these topics, the model's internal representations are fuzzed, effectively making it 'stupid' on command for specific domains.

Goodfire AI found that for certain tasks, simple classifiers trained on a model's raw activations performed better than those using features from Sparse Autoencoders (SAEs). This surprising result challenges the assumption that SAEs always provide a cleaner concept space.

Trying to simply block a model from learning an undesirable behavior is futile; gradient descent will find a way around the obstacle. Truly effective techniques must alter the loss landscape so the model naturally "wants" to learn the desired behavior.

Instead of only analyzing a fully trained model, "intentional design" seeks to control what a model learns during training. The goal is to shape the loss landscape to produce desired behaviors and generalizations from the outset, moving from archaeology to architecture.

Research shows it's possible to distinguish and remove model weights used for memorizing facts versus those for general reasoning. Surprisingly, pruning these memorization weights can improve a model's performance on some reasoning tasks, suggesting a path toward creating more efficient, focused AI reasoners.