Neural Language Interpreter Bridges Symbolic and Neural Program Synthesis
The Neural Language Interpreter (NLI) introduces a fresh solution to the enduring conflict between symbolic and neural techniques in program induction. This innovative system autonomously develops its own discrete, symbolic-like programming language from the ground up, identifying a set of fundamental operations. It employs a unique differentiable neural executor to process variable-length sequences of these operations, enabling the representation of programs without being restricted to a fixed number of computation steps. NLI effectively merges the strengths of symbolic methods, which excel in compositional generalization and data efficiency yet struggle with scalability, with neural networks that adapt well to data but falter in compositional contexts. This research, documented as arXiv:2604.18907v1, showcases a gradient-based program synthesis strategy utilizing neurally interpreted languages.
Key facts
- Neural Language Interpreter (NLI) learns its own discrete, symbolic-like programming language end-to-end
- NLI autonomously discovers a vocabulary of primitive operations
- Uses a novel differentiable neural executor to interpret variable-length sequences of primitives
- Allows representation of programs not bound to a constant number of computation steps
- Bridges symbolic and neural approaches in program induction
- Addresses trade-off between compositional generalization and scalability
- Overcomes limitations of domain-specific languages that are labor-intensive to create
- Research published as arXiv:2604.18907v1 with announcement type cross
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