A bias for simplicity by itself does not guarantee good generalization (see the No Free Lunch Theorems). So an inductive bias is only good to the extent that it reflects structure in the data. Is the world simple? The success of deep nets (with their intrinsic Occam's razor) would suggest yes(?)

Hi thanks for the comment! I'm not too familiar with the robot-learning literature but would love to learn more about it!

Thank you Andrew!! :)

On a personal note, this is my first full-length first-author paper! @ekdeepl.bsky.social and I both worked so hard on this, and I am so excited about our results and the perspective we bring! Follow for more science of deep learning and human learning!

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Thank you to amazing collaborators!
@ekdeepl.bsky.social @corefpark.bsky.social @gautamreddy.bsky.social @hidenori8tanaka.bsky.social @noahdgoodman.bsky.social
See the paper for full results and discussion! And watch for updates! We are working on explaining and unifying more ICL phenomena! 15/

💡Key takeaways:
3) A top-down, normative perspective offers a powerful, predictive approach for understanding neural networks, complementing bottom-up mechanistic work.

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💡Key takeaways:
2) A tradeoff between *loss and complexity* is fundamental to understanding model training dynamics, and gives a unifying explanation for ICL phenomena of transient generalization and task-diversity effects!

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💡Key takeaways:
1) Is ICL Bayes-optimal? We argue the better question is *under what assumptions*. Cautiously, we conclude that ICL can be seen as approx. Bayesian under a simplicity bias and sublinear sample efficiency (though see our appendix for an interesting deviation!)

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Ablations of our analytical expression show the modeled computational constraints, in their assumed functional forms, are crucial!

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And reveals some interesting findings: MLP width increases memorization, which is captured by our model as a reduced simplicity bias!

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Our framework also makes novel Predictions:
🔹**Sub-linear** sample efficiency → sigmoidal transition from generalization to memorization
🔹**Rapid** behavior change near the M–G crossover boundary
🔹**Superlinear** scaling of time to transience as data diversity increases

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Intuitively, what does this predictive account imply? A rational tradeoff between a strategy's loss and complexity!

🔵Early: A simplicity bias (prior) favors a less complex strategy (G)
🔴Late: reducing loss (likelihood) favors a better-fitting, but more complex strategy (M)

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Fitting the three free parameters of our expression, we see that across checkpoints from 11 different runs, we almost perfectly predict *next-token predictions* and the relative distance maps!

We now have a predictive model of task diversity effects and transience!

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We assume two well-known facts about neural nets as computational constraints (scaling laws and simplicity bias). This allows writing a closed-form expression for the posterior odds!

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We model our learner as behaving optimally in a hypothesis space defined by the M / G predictors—this yields a *hierarchical Bayesian* view:

🔹Pretraining = updating posterior probability (preference) for strategies
🔹Inference = posterior-weighted average of strategies

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We now have a unifying language to describe what strategies a model transitions between.

Back to our question:*Why* do models switch ICL strategies?! Given M / G are *Bayes-optimal* for train / true distributions, we invoke the approach of rational analysis to answer this!

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By computing the distance between a model’s outputs and these predictors, we show models transition between memorizing and generalizing predictors as experimental settings are varied! This yields a unifying view on known ICL phenomena of task diversity effects and transience!

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