# Highly Opinionated Advice on How to Write ML Papers - Neel Nanda (2025-05-12)

Source: https://www.lesswrong.com/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers
Author: Neel Nanda
Date: 12th May 2025
Fetch-status: excerpted from LessWrong HTML via browser.
Use: distillation and paper-writing evidence. This is adjacent to the research-process sequence, and directly useful when turning messy findings into a public artifact.

## Why this matters for agents

This post is the operational version of the distillation stage: compress the research into a few claims, red-team the evidence, write to inform rather than persuade, and spend disproportionate care on the abstract, intro, figures, and limitations.

## Quotes

> The essence of an ideal paper is the narrative: a short, rigorous and evidence-based technical story you tell, with a takeaway the readers care about.

> The first step is to compress your research into these claims.

> Experimental Evidence: This is absolutely crucial to get right and aggressively red-team, it’s how you resist the temptation of elegant but false narratives.

> Inform, not persuade: Avoid the trap of overclaiming or ignoring limitations.

> Your research only matters if people read, understand, and build upon it.

> At its core, a paper should present a narrative of one to three specific concrete claims that you believe to be true, that build to some useful takeaway(s).

> Readers will rarely take away more than a few sentences of content. Choose those sentences carefully.

> Generally, stronger statements make for more interesting papers, but require higher standards of evidence - resist the temptation to overclaim for clicks!

> Warning: Before moving into paper-writing mode, it's crucial to verify that your evidence is actually correct.

> Novelty means it expands our knowledge.

> Rigorous, at-scale replications of shaky results, negative results of seemingly promising hypotheses, and high-quality failed replications of popular papers are all very valuable contributions.

> A particularly important thing to get right is extensive red-teaming: you should spend a good amount of your time, both during the original research and now, red teaming your narrative.

> Good experiments distinguish between hypotheses.

> This skepticism and sanity checking is especially key for particularly surprising or novel bits of evidence.

> Ablation studies: When a paper introduces a complex new method, there are often several moving parts.

> Track pre/post-hoc analysis.

> Quality Over Quantity: Try to prioritise having at least one really compelling and hard to deny experiment, over a bunch of mediocre ones.

> Baselines are Crucial.

> The subtlety of baselines: It's not enough to just have them; you must strive to have the strongest possible baselines.

> The Guiding Question for Evidence: Ultimately, the question to ask about your evidence is: "Should this update a reader's beliefs about my claims?"

> Reproducibility & Publishing code: Rigour can be in the eye of the beholder: if readers cannot understand or verify it for themselves, it’s far harder to consider it rigorous.

> A key challenge in paper writing is the illusion of transparency - you have spent months steeped in the context of this research project.

## Source graph

Links visible in this post worth follow-up:
- Research process sequence: https://www.lesswrong.com/s/5GT3yoYM9gRmMEKqL
- Jakob Foerster writing advice: https://www.jakobfoerster.com/
- Jacob Steinhardt writing/research advice: https://cs.stanford.edu/~jsteinhardt/
- Refusal is mediated by a single direction: https://arxiv.org/abs/2406.11717
- Nanda grokking work: https://arxiv.org/abs/2301.05217
- Paper writing checklist: Google Docs link visible in post, not cached.
