yeah dude in-context learning is all you need don't worry. btw you gotta check out the new model it's better because they trained it a lot more on a bunch of stuff
11:49 AM · Jul 4, 2026 · 10.3K Views
Will Brown, Prime Intellect research lead, argues in-context learning is insufficient compared to extensive model pretraining
Story Overview
Prime Intellect research lead Will Brown frames in-context learning as falling short for cutting-edge performance and points instead to models that receive far more extensive pretraining across diverse data, sharpening the long-running tension between clever prompting and raw compute scale.
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Open Question
Training scale still sets the ceiling
Brown contrasts quick context tricks with the gains from training on much larger, varied datasets, leaving open whether any prompting method can close that gap without matching the underlying compute investment.
Developer Impact
Prime Intellect infrastructure ties into the claim
The company offers pretraining-as-a-service and RL tooling across thousands of environments, yet no specific new model, benchmarks, or availability details are tied to the July post.
Sentiment
Users in the replies sarcastically mocked claims that scaling to more parameters will overcome limitations in continual learning, though a few were optimistic about pairing in-context learning with enormous context windows.
Pos
33.3%
Neg
66.7%
9 comments with sentiment.
Cluster Engagement
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Jeff Huber@jeffreyhuber
@willccbb what’s the best way to reason about the limitations of in context learning?
will brown@willccbb
yeah dude in-context learning is all you need don't worry. btw you gotta check out the new model it's better because they trained it a lot more on a bunch of stuff
2hViews 579Likes 4Bookmarks 0
LIKES13
will brown@willccbb
@jeffreyhuber work with a coding agent across multiple compactions and get annoyed when you have to remind it stuff
Jeff Huber@jeffreyhuber
@willccbb what’s the best way to reason about the limitations of in context learning?
2hViews 477Likes 13Bookmarks 0
REPLIES3
Jeff Huber@jeffreyhuber
@willccbb sure but that just could be bad compaction / memory
assume perfect context - what’s the limit?
will brown@willccbb
@jeffreyhuber work with a coding agent across multiple compactions and get annoyed when you have to remind it stuff
2hViews 147Likes 1Bookmarks 0
Benjamin Glickenhaus@benglickenhaus
@willccbb but will what if all that new stuff makes it better at in context learning
2hViews 91Likes 1
will brown@willccbb
@benglickenhaus slightly
2hViews 87Likes 1
will brown@willccbb
@jeffreyhuber compressing many many trajectories into O(100K) tokens is always gonna be lossy, tokens are a very expensive form of memory in that a small number of bits gets expanded into a large memory size (KV) via a static transformation. vs model weights themselves have params ~= bits
2hViews 33Likes 1
Jeff Huber@jeffreyhuber
@willccbb i get all that!
i have a hard time reasoning about the task- specific ceiling.
2hViews 28Likes 1
retto@rettooooo
@willccbb what i think would be very powerful is in-context learning paired with a 10 bil context window
2hViews 42
Matt@Matthewagi
@willccbb Wow. I didn't know there's no silver bullet but engineering tradeoffs. You're telling me now for the first time
2hViews 33Likes 2
Eric W. Tramel@fujikanaeda
@willccbb i feel like we got so excited about ICL but it wasn’t really a strong effect and then we just kind of swept it under the rug and made more environments and bought some more books
2hViews 66Likes 1
Chase Brower@ChaseBrowe32432
@willccbb @benglickenhaus i think the needle is moving somewhat
2hViews 25Likes 1
Empyrian@empyriangaming
@willccbb More stuff -> better stuff 😌
1hViews 52
Benjamin Glickenhaus@benglickenhaus
@willccbb just another trillion parameters bro i promise the next trillion will solve continual learning
2hViews 15Likes 1
will brown@willccbb
@rettooooo i think it would be very powerful if everyone had a top floor luxury penthouse in manhattan
2hViews 41
Frame@btcframe
@willccbb Hard to argue with that, more data and training really does seem to be the winning formula.
2hViews 12
Colt Feltes@coltfeltes
@willccbb Yeah, dude - don’t worry about slowing down and actually doing your work. You’ll never have to even do the work with the new model, bro.
1hViews 10
Reyaa@snr_boost
@willccbb Doesn't matter. Egypt won
1hViews 9
Harsh.@freshlimesofa
@willccbb To vaguepost or to not vaguepost
2hViews 9
Shubhankar@_shubhankar
@willccbb 😂😂
57mViews 8
Youssef El Manssouri@yoemsri
@willccbb In-context learning is great until your context window fills up, costs spike, and latency kills the product.
Then fine-tuning starts looking pretty good.
1hViews 5
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