Good empirical safety research is heavily bottlenecked on thinking more clearly. This post shares a few frames and tips I found useful, illustrated with concrete examples.

I spent the better part of 2026 in empirical AI safety research apprenticeships. I was mentored by Alex Turner and Alex Cloud as part of MATS 9 Team Shard, where my collaborator and I worked on finetuning-based misalignment auditing. I then interned at Redwood Research, managed by Aryan Bhatt, where I worked on sabotage capability evaluations.

These thoughts are indebted to my mentors, collaborators, friends, and colleagues, but they are not direct reflections of their views.

Thank you to Alex Cloud, Alex Turner, Aryan Bhatt, Jeremy Huff, and especially to Keshav Shenoy for feedback on the piece.

Better models of the problem speed up empirical iteration

Fast iteration is one of the key virtues of an empirical safety researcher. At first, I didn't find it obvious what that meant or how I should get better at this skill. I imagined a sprinter, doing one looped motion at a far quicker pace than everyone else. I now think that's the wrong mental picture, and going fast is more like being an efficient grappler–agile, strategic [...]

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Outline:

(01:00) Better models of the problem speed up empirical iteration

(02:26) Develop predictive models and update them

(04:05) Update with uncertainty in mind

(05:51) Predict senior researchers' research takes

(07:34) Use metacognitive strategies to make predicting and updating habitual

(09:22) Spend more effort on research communication

(11:38) Writing good Slack update threads

(14:03) Making good plots

(15:30) Be calibrated about the process

(15:53) Good confusion feels different from bad confusion

(16:57) Sometimes it's better to burn money than time

(17:36) Small time commitments become costly in attention

(18:20) Know which level of 'being done' to aim for

(19:52) Closing Thoughts

The original text contained 3 footnotes which were omitted from this narration.

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First published:
October 12th, 2026

Source:
https://www.lesswrong.com/posts/Q4rcvgjbgjTwmn8Xa/research-lessons-i-learned-at-mats-and-redwood

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Narrated by TYPE III AUDIO.

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Images from the article:

Fig 1: The mentor starts off with a bit more context than the IC (individual contributor), but the IC continuously gains more context, so it’s particularly important for the IC to get the mentor back in the game quickly and regularly with every research update. I drew a sketch and Astra made it nice.

Fig 2: Plots that fit existing templates are way easier to understand. I give some examples of plot templates and what they tend to communicate. The data is synthetic and meant to illustrate the main points. I made the figures with Astra.

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“Research Lessons I Learned at MATS & Redwood” by Jo Jiao

LessWrong (30+ Karma)

“Research Lessons I Learned at MATS & Redwood” by Jo Jiao