How to Write a NeurIPS / ICML Rebuttal That Actually Changes Scores

At major machine-learning conferences, you get a short window to respond to reviews, and a strict word or character limit. The reviewers and the area chair are reading many rebuttals in a short time. Your job is not to answer every sentence of every review. It is to change the minds of the people whose scores decide your paper.

Step 1: Triage before you write

Read all the reviews together, then sort every concern into three piles:

• Score-moving concerns: missing baselines, doubts about novelty, a flaw in the evaluation, an unclear key claim. These decide the outcome.
• Fixable misunderstandings: the reviewer missed something that is in the paper. Cheap to fix, and often decisive.
• Minor points: typos, formatting, small requests. Answer briefly at the end.

Look for concerns raised by more than one reviewer. Those are the ones the area chair will notice, and they belong at the top of your rebuttal.

Step 2: Lead with your strongest evidence

Put the two or three most important concerns first, and answer each with evidence in the rebuttal itself: a new number, a small table, a specific clarification with a section reference. Mark new results clearly so they are easy to find.

(R1, R3) Missing baseline X.
We ran X on [dataset]. New result: X reaches 71.2 vs. ours 74.8 (mean of
3 seeds, table below). Our method remains better because [one-line reason].
We will add this comparison to Section 5.

(R2) Novelty relative to Y.
Y differs in [specific way]: it [what Y does], whereas ours [what yours does].
We will add a paragraph to Related Work making this explicit.

Step 3: Handle the rest briefly

Group the remaining points in a short “Minor points” list, one line each. Confirm what you will fix (typos, missing standard deviations) and what you will not, with a brief reason. Do not leave a reviewer's comment unaddressed just because it seems small: it can look like you ignored them.

Tone: what works

• Thank the reviewers in one sentence, then move on. Long thanks waste your limit.
• Never attack. If a reviewer misread the paper, say “we should have made this clearer” and point to the exact section. Do not say the reviewer is wrong.
• Concede fair points quickly. A quick “this is a valid limitation; we will state it in Section 6” builds more trust than a long defence.
• Do not overpromise. Only promise changes you will actually make in the camera-ready version. If a reviewer follows up on a promise you did not keep, you lose credibility.

What not to do

• Do not spend your limit on a single small point. Prioritise ruthlessly.
• Do not add new claims that were not in the paper. It can look like you are changing the paper during review.
• Do not pad. Reviewers reward clarity. Dense, specific answers beat long, general ones.
• Do not ask reviewers to change their score or complain about their competence. It never helps.
• Do not ignore the area chair. They often make the final call, so write so that someone who only reads your rebuttal and the reviews can follow your reasoning.

If you are still rejected

Take the reviews seriously and use them. Reviewers at one venue often raise the same problems as reviewers at the next. Fix the real weaknesses (baselines, clarity, evaluation) before you resubmit, rather than resubmitting unchanged. Papers improve most between submissions, not within a single review round.