ICML 2026 Reviewer Instructions

ICML 2026 Reviewer Instructions Thank you for serving as a reviewer for ICML 2026! The commitment and time investment of the program committee are essential to the success of ICML, and we are deeply grateful for your effort. Key Information Reviewing Principles, Tips, and Best Practices Reviewer Form Instructions: Main Track Reviewer Form Instructions Position Track Reviewer Form Instructions Details of the Reviewing Process Key Information Important Contacts The area chair (AC) assigned to a paper should be your first point of contact for that paper. You can contact the AC by leaving a comment in OpenReview with the AC as a reader. Senior area chairs (SACs) and program chairs (PCs) will also be listed as readers, but will not be notified. If you encounter a situation that you are unable to resolve with your AC, please contact the program chairs. Please refrain from writing to the program chairs at their own email addresses. Responsibilities of Reviewers The responsibilities of a reviewer for ICML are as follows: Indicate areas of research expertise and “bid” on submissions to review. Check reviewing assignments and notify the overseeing area chair of any problems (e.g., conflicts of interest). Carefully review the correctness and merits of the assigned submissions. Follow their assigned actual policy for LLM use in reviewing, displayed on their Reviewer Console . Read and acknowledge the Authors’ Responses. Actively participate in discussions. There are three rounds in the author-reviewer discussion (rebuttal, reviewer follow-up, author follow-up), each limited to 5000 characters. Important Dates Here is a tentative timeline of the reviewing process. All deadlines are midnight AOE: Bidding period : January 27 - February 2, 2026 Full paper submission deadline: January 28, 2026 Submission assignment period: January 29 - February 11, 2026 Reviewing period : February 12 - March 12, 2026 Deadline for reviews : March 12, 2026 Reviewer-Author discussion period : March 24 - April 7, 2026 Deadline to acknowledge authors’ response : April 3, 2026 Reviewer-AC discussion period : March 31 - April 12, 2026 Author notification: April 30, 2026 Ethical Conduct of Peer Review: Members of the program committee, including reviewers, are expected to follow Peer Review Ethics 2026 . In particular: All information related to submitted manuscripts (along with reviews and discussion) is confidential . Do not use ideas, code, or results from submissions in your own work until they become publicly available. Do not talk about or share submissions with anyone without prior approval from the program chairs. Code submitted for review cannot be distributed or used for any other purpose. Any form of collusion, whether explicit or tacit (e.g., an arrangement between authors and reviewers, ACs, or SACs to obtain favorable reviews), is forbidden. If you believe someone may be engaging in unethical conduct, please notify ICML via the Ethics Violation Reporting form . All suspected unethical conduct will be investigated by the program chairs, the integrity chair, or the ICML Oversight Committee. Individuals found violating the rules may face sanctions and/or have their submissions rejected (see Peer Review Ethics 2026) . Generative AI Considerations: Reviewers must follow their assigned actual policy for LLM use in reviewing, displayed on their Reviewer Console . (For details, see the Policy for LLM use in reviewing ). Authors are allowed to use generative AI tools such as LLMs to assist in writing or research, but they remain responsible for all content in their paper, including any AI-generated content that might be construed as plagiarism or scientific misconduct. The latter includes submission of low-quality AI-generated content (AI slop). If you suspect this is the case, please report it via the Ethics Violation Reporting form . Prompt injection by authors is forbidden. ICML integrity chairs and program chairs will use prompt-injection detectors to ensure compliance. However, if you suspect anything, please report it via the Ethics Violation Reporting form and review the rest of the paper as normal. (Update 2/14/2026): While prompt injection by authors is disallowed, we will not penalize papers with prompts that merely seek to detect the use of LLMs by reviewers. Reviewing Principles, Tips, and Best Practices (Adapted from the ICML 2022 Reviewer Tutorial .) "Review the papers of others as you would wish your own to be reviewed." – Mihir Bellare, IACR Distinguished Lecture: Caught in Between Theory and Practice Guiding Principles and Professionalism The guiding principle for reviewing is that reviewing should create value for: the authors, by giving them actionable feedback to potentially improve their papers. the community, by helping authors improve their papers and helping with the decisions to publish papers that advance our field. A critical aspect of this is the professional conduct of all ICML reviewers. Reviewers are expected to be polite, respectful, and overall professional in their conduct during the whole process. Unprofessional reviews can harm the community in multiple ways: frustration for authors (particularly students) who may slow down in their research, drop out of the field, and/or end up reviewing unprofessionally themselves as a result; loss of promising ideas that could advance the community; resubmission of very similar versions of the paper due to lack of constructive feedback; and so on. Best Practices Be thoughtful . The paper you are reviewing may have been written by a first-year graduate student who is submitting to a conference for the first time, and you don't want to crush their spirits. Be fair . Do not let personal feelings affect your review. Be useful . A good review is useful to all parties involved: authors, other reviewers, and AC/SACs. Try to keep your feedback constructive when possible. Be specific . Do not make vague statements in your review, as they are unfairly difficult for authors to address. Be flexible . The authors may address some points you raised in your review during the discussion period. Make an effort to update your understanding of the paper when new information is presented, and revise your review to reflect this. Be timely . Please respect the deadlines and respond promptly during the discussion.  If you cannot complete your review on time, please let the AC know as soon as possible. Please avoid biasing your review according to discriminatory criteria not having to do with scientific content or clarity. Please avoid wording that may be perceived as rude or offensive. If someone pressures you into providing a positive or negative review for a submission, please notify program chairs right away. If you notice unethical or suspect behavior, please report it via the Ethics Violation Reporting form . (Best practices are adopted from NeurIPS 2025 reviewer guidelines ) Tips for Reviewing Before starting to review a paper, (re-)read the Review Form, and think about the aspects of the paper that need to be evaluated. Read the paper carefully, critically, and with empathy. As you read, keep in mind that you will need to provide an evaluation of the paper via the Review Form, so it will likely help to take notes for yourself (e.g., highlight the main contributions, mark sections you will need to re-read or check more carefully in a subsequent pass). After reading the paper, think carefully about whether the paper has properly substantiated the claimed contributions . This may involve verifying proofs, checking whether hypotheses are actually tested by the experiments, checking whether empirical claims do indeed follow from empirical results, etc. This is often the most time-consuming part of the reviewing process. Good judgment is needed to determine the severity of any issues that you identify. It is helpful to point out minor issues that are easily fixed, but it is more important to focus on major issues that are critical to the main contributions. Consider whether the paper places the research presented into the context of current research . Assessments about a paper’s “originality” and “significance” often crucially depend on how the paper compares to prior works, and thus, such prior works should be cited and discussed in the paper. Note that in many cases, it is difficult and often unnecessary to cite all related prior works. If some relevant prior works are missed, then think about whether or not including them would change the conclusions of the paper. Some omissions may be considered minor issues that are easily fixed. Please give constructive comments and suggestions to the authors to help them potentially improve their paper. In particular, any comments about strengths and weaknesses must be substantiated. Other Resources Please see the ICML 2022 Reviewer Tutorial and Peer Reviewer Guidelines, Memefied for more tips, suggestions, and resources. Main Track Reviewer Form Instructions You will be asked on the review form to answer several questions for each paper. Below, we provide guidance on what to consider when answering these questions. Please keep in mind that after decisions have been made, reviews and meta-reviews of accepted papers and opted-in rejected papers will be made public. Reviews should therefore be constructive, professional, and respectful. Summary Briefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary. This is also not the place to paste the abstract; please provide the summary in your own understanding after reading. Strengths and Weaknesses Please provide a thorough assessment of the strengths and weaknesses of the paper, touching on each of the following dimensions: soundness, presentation, significance, and originality. We encourage you to be open-minded about the potential strengths and broad definitions of significance and originality . For example, originality may arise from creative combinations of existing ideas, application to a real-world use case, or removing restrictive assumptions from prior theoretical results. We provide detailed guidelines below on each dimension: Soundness : Is the submission technically sound? Are claims well supported (e.g., by theoretical analysis or experimental results)? Are the methods used appropriate? If the paper includes theoretical results, are the proofs correct and based on reasonable assumptions? If the paper includes empirical results, are the experiments well-designed? Are the authors careful and honest about evaluating both the strengths and weaknesses of their work? Note: Soundness is distinct from impact. A paper can be technically sound—meaning correct, rigorous, and methodologically appropriate—even if its contributions are modest or incremental. Conversely, a paper proposing a high-impact idea must still meet the same bar for technical soundness. Reviewers should assess these dimensions separately. Presentation : Is the submission clearly written and well structured? (If not, please make constructive suggestions for improving its clarity.) Is the overall narrative easy to follow? Does the work properly position itself in the context of prior/concurrent literature and clearly discuss how it differs? (Note that a superbly written paper provides enough information for an expert reader to reproduce its results.) Significance : Does the paper address an important or relevant problem? Does it advance understanding, capabilities, or practice in machine learning? Could it influence future research or applications (e.g., other researchers or practitioners are likely to use the ideas or build on them)? Is the scope of impact broad or specialized, and is that appropriate for the contribution? Even if the improvements are modest or domain-specific, could they unlock new directions or provide practical utility? Originality : Does the work provide new insights, deepen understanding, or highlight important properties of existing methods? Does the work introduce new tasks, methods, theory, data, or perspectives that advance the field in some dimensions? Does this work offer a novel combination of existing techniques, and is the reasoning behind this combination well-articulated? Are the contributions clearly distinguished from closely related literature, and is the novelty well justified? As the questions above indicates, originality does not necessarily require introducing an entirely new method. Rather, a work that provides novel insights by evaluating existing methods, or demonstrates improved understanding is also equally valuable. Soundness Based on what you discussed in “Strengths and Weaknesses”, please rate the paper on the following scale to indicate the soundness of the technical claims, experimental and research methodology, and whether the central claims of the paper are adequately supported with evidence. If you select “fair” or “poor” (indicating that the paper falls short of the standard), ensure that “Strengths and Weaknesses” include a clear justification of your rating. 4: excellent 3: good 2: fair 1: poor Presentation Based on what you discussed in “Strengths and Weaknesses”, please rate the paper on the following scale to indicate the quality of the presentation. This should take into account the writing style and clarity, as well as contextualization relative to prior work. If you select “fair” or “poor” (indicating that the paper falls short of the standard), ensure that “Strengths and Weaknesses” include a clear justification of your rating. 4: excellent 3: good 2: fair 1: poor Significance Based on what you discussed in “Strengths and Weaknesses”, please rate the paper on the following scale to indicate the significance of the overall contribution this paper makes to the research area being studied. If you select “fair” or “poor” (indicating that the paper falls short of the standard), ensure that “Strengths and Weaknesses” include a clear justification of your rating. 4: excellent 3: good 2: fair 1: poor Originality Based on what you discussed in “Strengths and Weaknesses”, please rate the paper on the following scale to indicate its originality. If you select “fair” or “poor” (indicating that the paper falls short of the standard), ensure that “Strengths and Weaknesses” include a clear justification of your rating. 4: excellent 3: good 2: fair 1: poor Key Questions for Authors If you have any important questions for the authors, please carefully formulate them here (ideally around 3-5). Please reserve your questions for cases where the response would likely change your evaluation of the paper, clarify a point in the paper that you found confusing, or address a critical limitation you identified. Please number your questions so authors can easily refer to them in the response, and explain how possible responses would change your evaluation of the paper. Limitations Have the authors adequately di