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Lookahead: slow-weight interpolation around inner optimizers

Contribution grade

Neural-network optimization · Human-designed research method or resource

Reference score85,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Maintained fast weights updated by an inner optimizer and periodically interpolated/reset them toward slower weights, studying variance and empirical training stability.

Scientific scope

Maintained fast weights updated by an inner optimizer and periodically interpolated/reset them toward slower weights, studying variance and empirical training stability. Inherits inner SGD/Adam and iterate averaging; not universally accelerated optimization or negligible total memory. Additional slow weights and synchronization are real costs. Distinct from unrelated LookaheadDecoding inference methods.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits inner SGD/Adam and iterate averaging; not universally accelerated optimization or negligible total memory. Additional slow weights and synchronization are real costs. Distinct from unrelated LookaheadDecoding inference methods.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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