gift

10 notes carry this tag — 6 claims, 2 questions, 2 captures. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

Does GIFT's claimed 7.6% Llama-600M pretraining speedup (and its anisotropy-distortion diagnosis) hold up under peer review or independent replication?

capture promoted 2026-08-07 · gradient-geometry, quantization, distributed-training, quant-verification, primary-source-verification
As of 2026-08-07, GIFT's 7.6% Llama-600M speedup remains an unrefereed, unreplicated preprint figure

claim seedling Tier 1 2026-08-07 · gradient-geometry, quantization, distributed-training, gift, peer-review-status
Metis (2025) diagnoses anisotropy across weight, activation, and gradient spectra as a barrier to low-bit LLM training — a different mechanism from GIFT's, predating it by ~10 months, and not a replication of GIFT's claim

claim seedling Tier 1 2026-08-07 · gradient-geometry, quantization, low-precision, anisotropy, llm-pretraining
GIFT's G = E[δδ⊤] K-FAC factor is built from ordinary real-label backpropagated gradients, matching Kunstner et al.'s empirical-Fisher definition rather than the true-Fisher convention Amari and Martens & Grosse specify — softening the GIFT–Amari 'same object' identity

claim seedling Tier 1 2026-07-27 · gradient-geometry, fisher-information, empirical-fisher, k-fac, natural-gradient
Capture: Is GIFT's K-FAC gradient factor G = E[δδ⊤] the true Fisher (model's sampled predictive distribution) or the empirical Fisher (real training labels) — and does the difference weaken the GIFT–Amari 'same object' bridge?

capture promoted Tier 1 2026-07-26 · gradient-geometry, fisher-information, empirical-fisher, k-fac, natural-gradient
GIFT's near-isotropic gradient transform is derived from the Fisher information matrix via the same K-FAC factorization that underlies Amari's natural gradient

claim seedling Tier 1 2026-07-25 · gradient-geometry, natural-gradient, fisher-information, k-fac, anisotropy
GIFT uses the Fisher/K-FAC object only to define communication coordinates and explicitly, repeatedly denies being a natural-gradient or K-FAC optimizer

claim seedling Tier 1 2026-07-25 · gradient-geometry, natural-gradient, fisher-information, k-fac, quantization
Is GIFT's K-FAC gradient factor G = E[δδ⊤] the true Fisher (model's sampled predictive distribution) or the empirical Fisher (real training labels) — and does the difference weaken the GIFT–Amari 'same object' bridge?

question answered 2026-07-25 · gradient-geometry, fisher-information, empirical-fisher, k-fac, natural-gradient
GIFT (2026) diagnoses gradient anisotropy as why Euclidean low-precision quantization degrades LLM pretraining — and fixes it by transforming gradients to a near-isotropic space first

claim seedling Tier 1 2026-07-09 · gradient-geometry, distributed-training, quantization, low-precision, anisotropy
Does GIFT's claimed 7.6% Llama-600M pretraining speedup (and its anisotropy-distortion diagnosis) hold up under peer review or independent replication?

question open 2026-07-09 · gradient-geometry, quantization, distributed-training, quant-verification, primary-source-verification