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Large language models (LLMs) have demonstrated remarkable abilities inrepresentation learning for program synthesis and understanding tasks. Thequality of the learned representations appears to be dictated by the neuralscaling laws as a function of the number of model parameters and observations,while imposing upper bounds on the model performance by the amount of availabledata and compute, which is costly.  In this study, we attempt to render the training of LLMs for programsynthesis more efficient by unifying four key components: (1) modelarchitectures, (2) learning methods, (3) infill sampling, and, (4) datadistributions. Specifically, for the model architecture, we attempt to unifyencoder and decoder-based models into a single prefix-LM. For learning methods,(i) causal language modeling, (ii) span corruption, (iii) infilling are unifiedinto a simple learning algorithm. For infill sampling, we explore the claim ofa "free lunch" hypothesis. For data distributions, the effect of a mixturedistribution and multi-epoch training of programming and natural languages onmodel performance is explored.  We conduct a comprehensive series of empirical experiments on 1B LLMs, forwhich failures and successes of this exploration are distilled into fivelessons. We will provide a final recipe for training and release CodeGen2models in size 1B, 3.7B, 7B, and, 16B parameters, along with the trainingframework as open-source: https://github.com/salesforce/CodeGen.

CodeGen2: Lessons for Training LLMs on Programming and Natural Languages