Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Grokking at the Edge of Linear Separability
Authors: Alon Beck, Noam Itzhak Levi, Yohai Bar-Sinai
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Fig. 1, we show numerical results depicting the gradient-descent dynamics of the model across three values of λ d/N. Notably, we observe a significant grokking effect, both in the non-monotonicity of the test loss, and a delayed rise in test accuracy, only when λ λc = 1/2. (...) In Fig. 4 we present numerical simulations supporting this behavior. |
| Researcher Affiliation | Academia | 1Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel 2École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Correspondence to: Alon Beck <EMAIL>, Noam Levi <EMAIL>. |
| Pseudocode | No | The paper describes methods using mathematical equations and text, but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about releasing code or provide links to a code repository. |
| Open Datasets | No | The paper describes generating a synthetic dataset: "We study a typical logistic binary classification problem, with the goal of finding a linear separator between two Gaussians with distinct labels." and "xi N(0, σ2Id)". It does not use or provide access to any pre-existing public datasets. |
| Dataset Splits | Yes | The parameters are N = 4 104,σ = 5, η = 0.01. The number of test samples is Ntest = 104. Additional details regarding the experiments can be found in App. K. |
| Hardware Specification | No | The paper does not specify any particular hardware (CPU, GPU, etc.) used for running the experiments. |
| Software Dependencies | No | The paper mentions "using adaptive momentum based optimizers like ADAM (Kingma & Ba, 2017)" in Section 3.5 and "ADAM optimizer with Py Torch s default parameters" in Fig. 11, but it does not specify version numbers for these software components. |
| Experiment Setup | Yes | The parameters are N = 4 104,σ = 5, η = 0.01. The direction of S(t = 0) was drawn isotropically with S0 = 0.1 and b(t = 0) = 0. (...) using ADAM optimizer (with β1 = 0.8, β2 = 0.9), instead of GD. The parameters are λ = d/N = 0.495, N = 4000 and σ = 1. |