Are modern ML PhDs becoming too incremental, or is this just what research looks like now? [D]
Our take
The concerns raised by this ML PhD about the state of doctoral research in machine learning touch on something many in the field are quietly grappling with. As Has industry effectively killed off academic machine learning research in 2026? observes, the landscape has shifted dramatically, and when combined with the growing frustration that ML conference reviews sometimes feel like a "lottery", we see a troubling pattern emerging where quantity and presentation polish may be overshadowing intellectual depth. The question isn't whether incremental progress has value—of course it does—but whether the current system adequately distinguishes between work that meaningfully advances our understanding and work that simply produces another temporary boost on a leaderboard.
What makes this particularly challenging is that machine learning has evolved into an extraordinarily empirical field, where the tools for exploration are more powerful than ever, yet the pathways to genuine insight aren't always clear. When a PhD student can run dozens of experiments across multiple domains, carefully tune hyperparameters, and present clean results that claim state-of-the-art status, the bar for what constitutes a "contribution" risks becoming conflated with computational effort and presentation quality. This creates a perverse incentive structure where the most publishable deltas—the small method variations and clever combinations—become prioritized over the harder work of identifying fundamental principles or reusable frameworks. The field's rapid pace, while exciting, may be amplifying this dynamic by making it easier to build on existing work without always pushing it forward in meaningful ways.
The distinction between strong incremental work and a collection of polished benchmark papers lies in the presence of transferable insights. A genuinely valuable incremental contribution asks not just "does this work better?" but "why does it work better?" and "can others apply this understanding elsewhere?" It establishes evaluation protocols that become standards, identifies failure modes that guide future development, or reveals mechanisms that deepen theoretical understanding. When the core contribution is primarily about beating a specific metric on a particular dataset through careful engineering, we're essentially producing sophisticated variations on existing themes rather than building toward a more coherent scientific foundation. This isn't necessarily the fault of individual researchers—it reflects systemic pressures around publication, funding, and career advancement that reward visible progress over invisible understanding.
Perhaps the most constructive path forward involves rethinking how we evaluate and reward doctoral work in machine learning. Rather than treating each paper as a standalone contribution, what if we emphasized the coherence of the entire dissertation as a body of work that collectively advances the field? This might mean valuing the identification of general principles, the creation of reusable tools or evaluation methods, or the systematic exploration of failure modes—even when these don't translate immediately into impressive benchmark numbers. The goal shouldn't be to discourage incremental work, but to ensure that such work is genuinely incremental in understanding, not just in methodology. As we look ahead, the field's ability to balance empirical innovation with scientific rigor will likely determine whether machine learning continues to mature as a discipline or remains perpetually focused on the next technical refinement rather than the next conceptual breakthrough.
I’ve been thinking about the current state of machine learning PhDs, including my own work, and I’d like to hear how others see it.
My impression is that a large fraction of modern ML PhD work follows a fairly predictable pattern: take an existing idea, connect it to another existing idea, apply it in a slightly different setting or community, tune the system carefully, add some benchmark results, and present the method as a new state-of-the-art approach. Another common pattern is mostly empirical: run benchmarks, report observations, provide some analysis, and frame that as the main contribution.
To be clear, I’m not saying this work is useless. Incremental progress matters, and not every PhD needs to invent a new paradigm. But sometimes it feels like many ML PhDs are closer to extended master’s theses: more experiments, more compute, more polished writing, and more benchmarks, but not necessarily a deeper scientific contribution.
What bothers me is that the same pattern appears even in top-tier conference papers. A paper may look strong because it has a clean story, a benchmark win, and good presentation, but after removing the “SOTA” claim, it is not always clear what lasting knowledge remains. Did we learn something general? Did we understand a mechanism better? Did we identify a failure mode? Did we create a reusable method or evaluation protocol? Or did we mostly produce another temporary leaderboard improvement?
I’m also reflecting this back onto my own PhD. I see some of the same patterns in my work, so this is not meant as an attack on others. It is more of a concern about the incentives of the field. ML seems to reward publishable deltas: small method variations, new combinations, benchmark improvements, and convincing empirical stories. But I’m less sure whether it consistently rewards deeper understanding.
So my question is:
Have ML PhDs become lower-quality compared to PhDs in other fields, or is this simply the normal shape of cumulative research in a fast-moving empirical field?
And maybe more importantly:
What separates a genuinely strong incremental ML PhD from one that is basically a collection of polished benchmark papers?
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- Why ML conference reviews sometimes feel like a “lottery“ [D]I’ve been trying to make sense of all the “ML conferences are a lottery” takes, and honestly I think it’s both true and not true depending on what you mean. If a paper is clearly strong, like genuinely solid contribution, well executed, easy to understand, it usually gets in. And if it’s clearly weak, it usually gets filtered out. The weirdness people complain about mostly lives in the huge middle where papers are good but not undeniable. That’s also where scale starts to matter. There are just so many submissions now that reviewers are stretched thin, matching isn’t perfect, and everyone has slightly different standards or taste. Add tight timelines and limited back-and-forth, and small things start to matter a lot. Whether a reviewer really “gets” your contribution, how clearly you framed it, or even just how it lands with that particular set of reviewers can swing the outcome. I think that’s why it feels random. Not because the whole system is broken, but because a big chunk of papers are sitting right near the decision boundary, and decisions there are naturally high-variance. People often from strong research groups don’t experience this. It’s more that they’re better at pushing their papers out of that borderline zone. Cleaner writing, stronger positioning, more predictable execution. So a larger fraction of their work is clearly above the bar. So my current take is: it’s not a lottery overall, but it absolutely behaves like one near the cutoff, and that’s where most of the frustration comes from. submitted by /u/Hope999991 [link] [comments]