The paper offers a formal proof that gradient descent on the logistic loss converges very slowly to the hard SVM solution in the case where the data are linearly separable. This result should be viewed in the context of recent attempts at trying to understand the generalization ability of neural networks, which have turned to trying to understand the implicit regularization bias that comes from the choice of optimizer. Since we do not even understand the regularization bias of optimizers for the simpler case of linear models, I consider the paper's topic very interesting and timely. The overall discussion of the paper is well written, but on a more detailed level the paper gives an unpolished impression, and has many technical issues. Although I suspect that most (or even all) of these issues can be resolved, they interfere with checking the correctness of the results. Unfortunately, in its current state I therefore do not consider the paper ready for publication. Technical Issues: The statement of Lemma 5 has a trivial part and for the other part the proof is incorrect: Let x_u = ||nabla L(w(u))||^2. - Then the statement sum_{u=0}^t x_u < infinity is trivial, because it follows directly from ||nabla L(w(u))||^2 < infinity for all u. I would expect the intended statement to be sum_{u=0}^infinity x_u < infinity, which actually follows from the proof of the lemma. - The proof of the claim that t*x_t -> 0 is incorrect: sum_{u=0}^t x_u < infinity does not in itself imply that t*x_t -> 0, as claimed. For instance, we might have x_t = 1/i^2 when t=2^i for i = 1,2,... and x_t = 0 for all other t. Definition of tilde{w} in Theorem 4:  - Why would tilde{w} be unique? In particular, if the support vectors do not span the space, because all data lie in the same lower-dimensional hyperplane, then this is not the case. - The KKT conditions do not rule out the case that \hat{w}^top x_n = 1, but alpha_n = 0 (i.e. a support vector that touches the margin, but does not exert force against it). Such n are then included in cal{S}, but lead to problems in (2.7), because they would require tilde{w}^top x_n = infinity, which is not possible. In the proof of Lemma 6, case 2. at the bottom of p.14:  - After the first inequality, C_0^2 t^{-1.5 epsilon_+} should be C_0^2 t^{-epsilon_+} - After the second inequality the part between brackets is missing an additional term C_0^2 t^{-\epsilon_+}. - In addition, the label (1) should be on the previous inequality and it should be mentioned that e^{-x} <= 1-x+x^2 is applied for x >= 0 (otherwise it might be false). In the proof of Lemma 6, case 2 in the middle of p.15:  - In the line of inequality (1) there is a t^{-epsilon_-} missing. In the next line there is a factor t^{-epsilon_-} too much. - In addition, the inequality e^x >= 1 + x holds for all x, so no need to mention that x > 0. In Lemma 1:  - claim (3) should be lim_{t \to \infty} w(t)^\top x_n = infinity - In the proof: w(t)^top x_n > 0 only holds for large enough t. Remarks: p.4 The claim that "we can expect the population (or test) misclassification error of w(t) to improve" because "the margin of w(t) keeps improving" is worded a little too strongly, because it presumes that the maximum margin solution will always have the best generalization error. In the proof sketch (p.3):  - Why does the fact that the limit is dominated by gradients that are a linear combination of support vectors imply that w_infinity will also be a non-negative linear combination of support vectors? - "converges to some limit". Mention that you call this limit w_infinity Minor Issues: In (2.4): add "for all n". p.10, footnote: Shouldn't "P_1 = X_s X_s^+" be something like "P_1 = (X_s^top X_s)^+"? A.9: ell should be ell' The paper needs a round of copy editing. For instance:  - top of p.4: "where tilde{w} A is the unique" - p.10: "the solution tilde{w} to TO eq. A.2" - p.10: "might BOT be unique" - p.10: "penrose-moorse pseudo inverse" -> "Moore-Penrose pseudoinverse" In the bibliography, Kingma and Ba is cited twice, with different years.