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# My Research Process: Understanding and Cultivating Research Taste - Neel Nanda (2025-05-01)
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Source: https://www.lesswrong.com/posts/Ldrss6o3tiKT6NdMm/my-research-process-understanding-and-cultivating-research
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Author: Neel Nanda
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Date: 1st May 2025
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Fetch-status: excerpted from LessWrong HTML via browser plus cross-checked against local shared draft.
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Use: core research-taste evidence, especially for deciding whether this should become a separate skill.
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## Why this matters for agents
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This post gives the boundary: research taste is not just picking ideas. It is judgment under long feedback loops across problem choice, exploration, experiment design, and distillation. It also explains why taste is learnable but slow: the feedback data is sparse.
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## Quotes
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> What is research taste? As I define it, research taste is far broader than just picking the right problem at the outset. Research is full of key decisions that will affect the future of the project, without an obvious way to find the right answer: from choosing the research problem itself, to identifying which anomalies are and are not worth exploring, distinguishing an experiment that will be compelling from one that’ll have inconclusive results, etc.
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> I think of taste as the set of intuitions and good judgment that guide a researcher’s decisions throughout the research process, any time an ambiguous or open-ended decision like this arises.
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> The core problem is you just don't get that much data. Generally the shorter a feedback loop is the more data you will get. By definition research taste is about things that are not immediately obvious.
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> I think the main way to speed it up is by getting more data, and by being more sample efficient about the data that you have.
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> When you have made a research decision and you eventually get feedback, do a post-mortem analyzing what did and did not work and why and what general themes you could look at in future.
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> As discussed, I define research taste broadly: it's the collection of intuitions and judgments that guide good decision-making throughout a research project, especially where feedback loops are long, and the search space is large and open-ended.
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> Exploration: A tactical sense for which experiments yield the most insight, recognizing interesting anomalies versus noise, knowing when to dig deeper or move on from a thread.
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> Understanding: Designing creative, elegant experiments that cleanly distinguish hypotheses, judging the plausibility and explanatory power of different theories, identifying crucial assumptions or potential confounds.
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> Communication & Distillation: Identifying the core, communicable claims within messy findings, structuring a compelling and true narrative, anticipating audience confusion, knowing what makes a result impactful to others.
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> The ideal is strategic conviction: the ability to adopt a confident mindset to maintain momentum, while regularly zooming out to reflect and maintaining the capacity for zoomed-out skepticism and the willingness to update or abandon course based on evidence.
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> Keep a research log. Ask why things worked or failed. Was it luck, execution, or a fundamental judgment call (taste)?
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> Papers are a biased dataset (publication bias!), but still useful.
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> Research taste isn't magic. It's a complex set of intuitions and frameworks built incrementally through experience, reflection, and learning from others. It governs the crucial, often implicit, decisions that shape a research project's success.
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> Because the feedback loops for high-level strategic taste are long and noisy, don't expect to master it quickly. It's perfectly normal, and indeed expected, to rely heavily on external guidance (like mentors or established research directions) early in your career. Focus first on mastering the skills with shorter feedback loops – coding, running experiments, analyzing data, clearly communicating simple results.
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> By actively engaging in research, deliberately reflecting on your decisions and their outcomes, and strategically leveraging the experiences of others, you can accelerate the development of your own research taste. Be patient with the process, especially the long-game aspects like problem selection. Trust that by doing the work and learning effectively from it, your intuition will improve over time.
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## Source graph
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High-value links inside this post:
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- Chris Olah, research taste / supervised data framing: https://colah.github.io/notes/taste/
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- Weekly reviews: https://www.neelnanda.io/blog/39-reflection
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- Activation patching paper: https://arxiv.org/abs/2309.16042
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- Gears-level model reference: https://www.lesswrong.com/posts/nEBbw2Bc2CnN2RMxy/gears-level-models-are-capital-investments
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