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Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning

A paper proposing a runtime representation for neuro-symbolic learning.

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Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning

By Lucas ShenemanarXiv
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The authors present Native Differentiable Virtual Machine (NDVM), a runtime representation that differentiates executable programs without compiling each candidate into a separate graph.

NDVM separates symbolic structure from differentiable numeric state, allowing for efficient evaluation of large populations of parameter vectors. The paper demonstrates the effectiveness of NDVM in co-search over LLM-proposed programs.

Abstract

The authors present Native Differentiable Virtual Machine (NDVM), a runtime representation that differentiates executable programs without compiling each candidate into a separate graph. NDVM separates symbolic structure from differentiable numeric state, allowing for efficient evaluation of large populations of parameter vectors. The paper demonstrates the effectiveness of NDVM in co-search over LLM-proposed programs.

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neuro-symbolic learningruntime representationdifferentiable virtual machineprogram evaluationparameter calibrationLarge Language ModelsAI AgentsContent EngineeringSemantic Interoperability
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