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The Developmental Landscape of In-Context Learning
We show that in-context learning emerges in transformers in discrete developmental stages, when they are trained on either language modeling or linear regression tasks.
Computational Complexity of Detecting Proximity to Losslessly Compressible Neural Network Parameters
To better understand complexity in neural networks, we theoretically investigate the idealised phenomenon of lossless network compressibility, whereby an identical function can be implemented with a smaller network.
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning
It is often very challenging to manually design reward functions for complex, real-world tasks.
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