How long does it realistically take for you to produce an ICML/NeurIPS/ICLR-level paper? [D]

2026-05-29 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Intermediate, short

Summary

A Reddit discussion among machine learning researchers reveals highly variable timelines for producing top-tier conference papers for venues like ICML, NeurIPS, and ICLR. While some papers are completed in as little as 3 weeks or 2 months, others can take 1 year, 1.5 years, or even 2+ years from initial idea to final acceptance. The consensus indicates that the actual research and idea development typically consume far more time than the writing phase, which often takes 1 to 2 months. Factors influencing these timelines include the number and experience of co-authors, the novelty of the idea, access to compute resources, and existing workload. One Area Chair noted that the visual and editorial quality of a paper significantly predicts its acceptance chances, often more than "mindless ablations."

Key takeaway

For AI Scientists and students aiming for ICML/NeurIPS/ICLR publications, recognize that while research can span 6 months to 2+ years, dedicating ample time to writing and presentation is crucial. Your paper's visual and editorial quality strongly influences reviewer perception and acceptance chances. Prioritize clear figures, correct grammar, and concise language over extensive, unrefined experimental results. This focus can significantly improve your submission's impact and reduce the risk of rejection due to poor presentation.

Key insights

Top-tier ML paper production timelines range from weeks to over two years, with research often exceeding writing time.

Principles

• Research phase dominates paper timelines.
• Presentation quality predicts acceptance.
• Team composition impacts speed.

In practice

• Prioritize clear writing and figures.
• Invest in editorial quality.
• Secure sufficient compute access.

Topics

• Machine Learning Research
• Academic Publishing
• ICML/NeurIPS/ICLR
• Research Timelines
• Scientific Writing
• Paper Acceptance

Best for: Research Scientist, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.