Why Is TMLR Getting Slower? The Review Crisis Facing a Machine Learning Journal

A PhD Student's Reviewing Nightmare

Recently, a PhD student nearing graduation sparked a widely resonant discussion in the machine learning community on Reddit (r/MachineLearning) about the reviewing speed of TMLR (Transactions on Machine Learning Research).

This solo-authored submission had been sent to TMLR several months prior. Things started off smoothly: reviewers submitted their feedback on time, the evaluations were highly positive, and only minor revisions were requested. But after the author submitted the revised manuscript, everything ground to a halt — the reviewers went silent, with only one confirming that their concerns had been addressed.

More than two months passed. Despite follow-up messages sent to both the Action Editor and the Editor-in-Chief, the paper's status showed absolutely no movement.

reddit source: Why is TMLR so slow in recent times

Why a Slow TMLR Review Is Especially Agonizing

For many researchers, TMLR was once seen as a "fast track" for publishing machine learning papers. This PhD student admitted that one key reason for choosing TMLR was the belief that its review cycle was shorter than that of traditional conferences and journals.

Career Implications for Solo Authors

This paper carries special significance — it is a solo-authored work. When applying for postdoctoral positions, a high-quality solo-authored paper is compelling evidence of a researcher's independent capabilities and a major asset on any CV.

With postdoc application deadlines looming, having a paper stuck in the review pipeline is an excruciating position to be in. This situation highlights a universal reality in academia: the misalignment between publication timelines and career milestones often places enormous pressure on early-career researchers.

How TMLR Works — and the Challenges It Now Faces

What Is TMLR?

TMLR is an open-access journal in machine learning, founded by prominent scholars. It operates on a review philosophy distinct from traditional conferences like NeurIPS, ICML, and ICLR:

No fixed submission deadlines — papers are accepted on a rolling basis year-round
Acceptance criteria centered on "technical correctness" and "supported claims" rather than novelty or impact
An emphasis on faster review turnarounds, ideally completing the process within a few months

This model initially attracted many researchers, particularly those looking to avoid the pain points of conferences — infrequent deadlines, low acceptance rates, and highly variable reviewer quality.

TMLR was officially launched in 2022, co-founded by Yoshua Bengio and other leading deep learning scholars, with support from the Machine Learning Foundation (MLF). Its core philosophy was inspired by the "continuous integration" concept from software engineering — papers submitted, reviewed, and published at any time, breaking the "publication bottleneck" caused by the one-or-two-deadlines-a-year structure of top venues like NeurIPS and ICML. TMLR's acceptance standards emphasize whether a paper's technical contributions are "well-supported by evidence," rather than relying on reviewers' subjective judgments about "importance" or "impact" — reducing review arbitrariness to some degree. The journal uses the OpenReview platform for open peer review, making the entire process transparent and traceable, another key feature that sets it apart from traditional journals.

Surging Submissions and a Shortage of Reviewers

As this case illustrates, TMLR's review speed is clearly slowing down. The reasons behind this are worth examining:

As TMLR's reputation has grown, submission volume has climbed steadily, but reviewer capacity has not kept pace. The broader problem of reviewer fatigue across the machine learning community is just as acute at TMLR. Once reviewers have completed their primary evaluation and given a positive assessment, they often lack the motivation to follow up on revised versions — and procrastination sets in.

Furthermore, TMLR's process relies heavily on Action Editors (AEs) to actively drive things forward. If an AE is overloaded or slow to respond, the entire process can stall — and authors have virtually no recourse beyond sending reminders.

Reviewer fatigue is a systemic crisis across the entire academic publishing ecosystem, and it is especially acute in machine learning. NeurIPS 2023 received over 12,000 submissions; ICLR 2024 also surpassed 10,000. This massive volume makes qualified reviewers extremely scarce. The deeper issue is the lack of meaningful incentives for reviewing itself — it is an unpaid academic obligation with no direct connection to promotions, grants, or other tangible rewards, leading to widespread delays, cursory reviews, and outright refusals. For TMLR specifically, the "second-pass review" of a revised manuscript is particularly unattractive: reviewers have already done the primary intellectual work, and confirming minor revisions feels like low-value repetitive labor — naturally pushed to the bottom of the priority list.

Deeper Problems in Academic Publishing

The Sustainability Problem of the "Fast Journal" Promise

This case raises a pointed question: any academic publishing model that depends on volunteer reviewers will face sustainability challenges when submission volumes surge. TMLR's original "speed" advantage was fundamentally built on the assumption of sufficient reviewer resources. Once that assumption breaks down, its promises become difficult to keep.

Practical Advice for Early-Career Researchers

For researchers in similar situations, this discussion offers several strategies worth considering:

Plan your publication timeline well in advance: Don't tie critical career milestones (like postdoc applications) too tightly to papers still under review — leave ample buffer time.
Make use of arXiv preprints: Posting your paper to arXiv early means you can showcase your work in application materials even if formal publication is delayed.
Communicate proactively but with restraint: Following up with the AE and Editor-in-Chief is reasonable, but understand the process constraints and avoid expecting immediate responses.
Diversify your submission strategy: Don't put all your eggs in one basket — thoroughly understand the pros and cons of each publication venue before committing.

Closing Thoughts

TMLR's founding vision — to provide the machine learning community with a fairer, more substance-focused publication venue — remains admirable. But this PhD student's experience is a reminder that the efficiency of any review system is constrained by the human resources behind it. As machine learning continues to grow explosively, the infrastructure of academic publishing — especially reviewer incentive mechanisms — urgently needs systemic rethinking and reform.

For early-career researchers, understanding these structural challenges and planning careers accordingly may be the most pragmatic approach available right now.