Anon: Extrapolating Adaptivity Beyond SGD and Adam
Quick summary
arXiv:2605.02317v3 Announce Type: replace Abstract: Adaptive optimizers such as Adam and non-adaptive methods like SGD exhibit distinct generalization capabilities across different architectures. Prior tunable optimizers attempt to bridge this gap by strictly interpolating between SGD and Adam, effectively confining adaptivity within the 0-to-1 bound. However, this restricted interpolation is fundamentally insufficient: we reveal that optimal adaptivity often requires extrapolation, such as negative adaptivity for classical CNNs and adaptivity of at least one ($\gamma \geq 1$) for Transformers
Key takeaways
- arXiv:2605.02317v3 Announce Type: replace Abstract: Adaptive optimizers such as Adam and non-adaptive methods like SGD exhibit distinct generalization capabilities across different architectures.
- Prior tunable optimizers attempt to bridge this gap by strictly interpolating between SGD and Adam, effectively confining adaptivity within the 0-to-1 bound.
- However, this restricted interpolation is fundamentally insufficient: we reveal that optimal adaptivity often requires extrapolation, such as negative adaptivity for classical CNNs and adaptivity of at least one ($\gamma \geq 1$) for Transformers
Why it matters
The importance of “Anon: Extrapolating Adaptivity Beyond SGD and Adam” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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