Line of inquiry
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How does scaling enable compositional generalization in neural networks?
A broader line of inquiry — a family of 11 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 11
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Where do neural networks still fail at compositional generalization despite scaling?
- Can scaling alone create compositional generalization without explicit binding mechanisms?
- Why does scaling data and model size improve compositional generalization?
- Does scaling model size solve compositional generalization problems?
- How does scaling and training data enable compositional behavior without symbolic mechanisms?
- Does compositional generalization emerge suddenly or improve smoothly with scale?
- What role does query-level exposure play in enabling compositional generalization?
- Does scaling data automatically produce compositional reasoning or just better feature encoding?
- Can recursion alone drive generalization better than model scale?
- What makes recursive structure different from other forms of compositional generalization?
- Why does gradient descent discover compositional structure without explicit pressure?