Generalization

The short of it Reinforcement‑learning fine‑tuning (RL‑FT) often looks like magic: you SFT a model until it aces your dataset, panic when it forgets math or coding edge cases, then run PPO and—voilà—generalization returns. A new paper argues the mechanism isn’t mystical at all: RL‑FT mostly rotates a model’s learned directions back toward broadly useful features, rather than unlocking novel capabilities. In practical terms, cheap surgical resets (shallow layers or top‑rank components) can recover much of that OOD skill without running an expensive RL pipeline. ...

Brains with Gradients: Why Energy-Based Transformers Might Be the Future of Thinking Machines AI models are getting better at mimicking human intuition (System 1), but what about deliberate reasoning—slow, careful System 2 Thinking? Until now, most methods required supervision (e.g., reward models, verifiers, or chain-of-thought engineering). A new architecture, Energy-Based Transformers (EBTs), changes that. It offers a radically unsupervised, architecture-level path toward models that “think,” not just react. The implications for robust generalization, dynamic reasoning, and agent-based autonomy are profound. ...