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Dan Braun

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2

NeurIPS Conference 2025 Conference Paper

Parameterized Synthetic Text Generation with SimpleStories

  • Lennart Finke
  • Chandan Sreedhara
  • Thomas Dooms
  • Mat Allen
  • Juan Rodriguez
  • Noa Nabeshima
  • Thomas Marshall
  • Dan Braun

We present SimpleStories, a large synthetic story dataset in simple language, consisting of 2 million samples each in English and Japanese. Through parameterizing prompts at multiple levels of abstraction, we achieve control over story characteristics at scale, inducing syntactic and semantic diversity. Ablations on a newly trained tiny model suite then show improved sample efficiency and model interpretability in comparison with the TinyStories dataset. We open-source all constituent parts of model creation, hoping to enable novel ways to study the end-to-end training process. As a byproduct, we move the frontier with regards to the fewest-parameter language model that outputs grammatical English.

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NeurIPS Conference 2024 Conference Paper

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

  • Dan Braun
  • Jordan Taylor
  • Nicholas Goldowsky-Dill
  • Lee Sharkey

Identifying the features learned by neural networks is a core challenge in mechanistic interpretability. Sparse autoencoders (SAEs), which learn a sparse, overcomplete dictionary that reconstructs a network's internal activations, have been used to identify these features. However, SAEs may learn more about the structure of the datatset than the computational structure of the network. There is therefore only indirect reason to believe that the directions found in these dictionaries are functionally important to the network. We propose end-to-end (e2e) sparse dictionary learning, a method for training SAEs that ensures the features learned are functionally important by minimizing the KL divergence between the output distributions of the original model and the model with SAE activations inserted. Compared to standard SAEs, e2e SAEs offer a Pareto improvement: They explain more network performance, require fewer total features, and require fewer simultaneously active features per datapoint, all with no cost to interpretability. We explore geometric and qualitative differences between e2e SAE features and standard SAE features. E2e dictionary learning brings us closer to methods that can explain network behavior concisely and accurately. We release our library for training e2e SAEs and reproducing our analysis athttps: //github. com/ApolloResearch/e2e_sae.

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v2026.09.13