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Life sciences · Preprint
arXiv · August 12, 2026
Raises a question worth testing. It does not answer one.
This preprint proposes epiplexity, a measure of structural information in data, as a training signal to guide domain selection and synthetic data generation for improved out-of-distribution generalization. The work is mechanistic and exploratory, presenting a hypothesis about what data properties enable transfer learning, supported by computational experiments rather than empirical validation on standard benchmarks or real-world tasks.
Preprint. Intervention: Epiplexity-guided online training signal for adaptive domain sampling weights and REINFORCE-guided synthetic data generation.
Higher epiplexity predicts improved downstream performance on zero-shot and fine-tuning based tasks Epiplexity-guided domain sampling using scaling law predictions supports improved out-of-distribution generalization REINFORCE-guided synthetic data generation toward epiplexity-maximizing distribution yields transferable representations
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This is a theoretical and methodological paper proposing epiplexity as a signal for data selection and generation, with supporting computational experiments but no clinical or real-world validation evidence.
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Modern systems are increasingly expected to transfer across tasks not specified during training. What data facilitates generalization in these new, unanticipated settings? One hypothesis is that data with more structural information could contain shared circuits and subprograms that could be recycled in a wider array of downstream settings. Epiplexity, a recently proposed measure of the structural information a compute-bounded learner can extract from data, provides a mechanism to reason about this relationship. In this paper, we show how to operationalize epiplexity as an online training signal for data selection and synthetic data generation. For selection, we fit scaling laws to the training loss curves of natural data domains to predict the expected epiplexity gain as a function of training tokens, and use this signal to adaptively determine the sampling weights over domains during training. For synthetic data generation, we define a generator's reward as the change in learner epiplexity over a buffer of previously generated data and use REINFORCE policy gradients to guide the generator toward an epiplexity-maximizing distribution. In both cases, higher epiplexity predicts improved downstream performance on zero-shot and fine-tuning based tasks, supporting the hypothesis that data rich in structural information yield representations that transfer across domains.
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