Gradient-Based Program Synthesis with Neurally Interpreted Languages
Summary: arXiv:2604.18907v1
Announce Type: cross
Introduction
Program induction has been a challenging area of research, primarily due to the inherent trade-off between symbolic and neural approaches. Traditional symbolic methods provide advantages in compositional generalization and data efficiency, yet they are hampered by scalability issues associated with domain-specific languages (DSLs). These languages are often labor-intensive to create and may not easily transfer to new domains. In contrast, neural networks are capable of learning flexibly from large datasets but often struggle with generalization in compositional and out-of-distribution contexts.
Proposed Solution: Neural Language Interpreter
The research introduces a novel architecture known as the Neural Language Interpreter (NLI), an instance of the Latent Adaptation Network. NLI is designed to autonomously discover its own discrete, symbolic-like programming language in an end-to-end fashion. This approach involves the following key features:
- Discovery of Vocabulary: NLI learns a vocabulary of primitive operations that allows it to construct programs autonomously.
- Differentiable Neural Executor: A new neural executor interprets variable-length sequences of these primitives, enabling the representation of programs that are not limited to a fixed number of computation steps.
- Gradient-Based Optimization: By employing the Gumbel-Softmax relaxation, the discrete, compositional structures of programs become amenable to gradient-based optimization, allowing for end-to-end model training.
Test-Time Adaptation
One of the most significant advancements of NLI is its ability to perform powerful test-time adaptation. During inference, NLI’s program inductor produces an initial guess for a program. This guess is subsequently refined through gradient descent via the neural executor, facilitating an efficient search for the neural program that best fits the provided data.
Performance Evaluation
The results from recent experiments indicate that NLI significantly outperforms existing methods such as in-context learning, test-time training, and continuous latent program networks. Specifically, NLI excels in tasks requiring combinatorial generalization and rapid adaptation to previously unseen tasks. These findings underscore the potential of NLI to effectively blend the compositionality of discrete programming languages with the gradient-based search capabilities and end-to-end learning paradigms of neural networks.
Conclusion
In conclusion, the Neural Language Interpreter represents a promising advancement in the field of program synthesis, addressing the limitations of both symbolic and neural methodologies. By combining the strengths of these approaches, NLI paves the way for the development of models that can achieve greater flexibility and efficiency in program induction tasks. This research establishes a new trajectory towards achieving a seamless integration of discrete and neural learning paradigms in artificial intelligence.