Are modern ML PhDs becoming too incremental, or is this just what research looks like now? [D]

2026-05-03 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Advanced, long

Summary

A Reddit discussion initiated by a PhD student questions whether modern Machine Learning (ML) PhDs have become overly incremental, often following a pattern of combining existing ideas, applying them in slightly new settings, tuning systems, and reporting benchmark wins. The author expresses concern that while incremental progress is valuable, many ML PhDs resemble extended master's theses, prioritizing empirical results and "state-of-the-art" claims over deeper scientific contributions or lasting knowledge. Commenters largely agree that incrementalism is a natural and often necessary part of PhD research across many fields, driven by publishing pressures, industry influence, and the inherent risk of pursuing groundbreaking but potentially fruitless long-term projects. Some suggest that ML, being a younger field, might even offer more opportunities for significant breakthroughs compared to more established disciplines like physics.

Key takeaway

For AI Scientists and PhD students navigating their research, recognize that while incremental progress is standard, your work should aim for lasting knowledge beyond mere benchmark wins. Prioritize identifying generalizable mechanisms, understanding failure modes, or creating reusable methods. Balance the pressure to publish with the pursuit of deeper scientific contributions to ensure your research has enduring impact.

Key insights

ML PhDs often prioritize incremental benchmark improvements due to academic and industry incentives.

Principles

• Research is inherently incremental.
• Highly competitive fields favor incrementalism.
• Publishing pressure drives short-term gains.

In practice

• Focus on generalizable knowledge over temporary SOTA claims.
• Investigate failure modes to deepen understanding.

Topics

• Machine Learning PhDs
• Incremental Research
• Research Incentives
• Publishing Pressure
• Benchmark Performance

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.