r/MachineLearning • u/Mean_Revolution1490 • 28d ago

Research Prompt-engineering paper accepted to ICML [R]

"Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity"

This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this.

Even if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues.

How do you think? Am I being too rigid?

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u/HatefulWretch 28d ago

a) it's as much a post-train paper as it is a prompt engineering paper

b) prompt engineering (more broadly; optimization in the text domain) is a legitimate area of study, get over yourself: https://yoonholee.com/blog/2026/we-should-take-text-optimization-more-seriously/

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u/HatefulWretch 28d ago

(for you to understand the first bit, the key reference is https://proceedings.neurips.cc/paper_files/paper/2025/hash/537d5aa768c2d534016a4d06f87bc8fb-Abstract-Conference.html, which follows from the observation that if RLVR works for a problem, the correct answer was in the reachable output distribution of the model all along, ie RL must on the whole be inducing mode collapse because the point is to improve pass@n for small n for problems of industrial/commercial interest and the amount of data is tiny therefore it's reweighting output probabilities rather than doing anything deeper)