Thinking Deeper, Not Longer: Depth-Recurrent Transformers for Compositional Generalization [R]

This content discusses a research paper on Depth-Recurrent Transformers, highlighting its findings on compositional and out-of-distribution generalization. It explores how intermediate step supervision can hinder genuine reasoning in AI models, making them overly reliant on statistical heuristics, a concept extended to foundation models and human intuition.