Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis (1805.04276v2)

Abstract: Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are trained to maximize the likelihood of known reference programs. While achieving impressive results, this strategy has two key limitations. First, it ignores Program Aliasing: the fact that many different programs may satisfy a given specification (especially with incomplete specifications such as a few input-output examples). By maximizing the likelihood of only a single reference program, it penalizes many semantically correct programs, which can adversely affect the synthesizer performance. Second, this strategy overlooks the fact that programs have a strict syntax that can be efficiently checked. To address the first limitation, we perform reinforcement learning on top of a supervised model with an objective that explicitly maximizes the likelihood of generating semantically correct programs. For addressing the second limitation, we introduce a training procedure that directly maximizes the probability of generating syntactically correct programs that fulfill the specification. We show that our contributions lead to improved accuracy of the models, especially in cases where the training data is limited.

• The paper introduces a novel framework that combines reinforcement learning with grammar-based syntax checks to address program aliasing during synthesis.
• It leverages syntax-based pruning and joint syntax learning to ensure generated programs are valid, significantly enhancing data efficiency.
• Experimental results on the Karel DSL demonstrate improved generalization and program efficiency compared to traditional MLE approaches.

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

The paper entitled "Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis" focuses on the development of an innovative framework for program synthesis, which amalgamates the strengths of grammar-based syntax enforcement and reinforcement learning (RL) techniques. The research addresses program synthesis from the perspective of automatically generating a program consistent with a specification, a task historically aligned with the goals of Artificial Intelligence research.

Key Contributions and Methodology

Two principal limitations of traditional neural program synthesis approaches are identified and targeted. Firstly, the issue of "Program Aliasing" is noted, highlighting how many syntactically different yet semantically equivalent programs can satisfy a given specification. Conventional sequence-to-sequence models, often inspired by neural machine translation, only maximize the probability of a single reference program, inadvertently penalizing correct alternatives. Secondly, these methods overlook the strict syntactic nature of programs, which could be employed to prune invalid program hypotheses efficiently.

To counteract these limitations, the authors propose a methodology that combines reinforcement learning with grammar-based syntax checks. This approach involves:

1. Reinforcement Learning (RL): To optimize the likelihood of generating any semantically correct program that fulfills the input-output specification. The policy gradient reinforcement learning paradigm allows the model to prioritize the generation of correct programs without being restricted to a single reference solution.
2. Syntax-Based Pruning: By leveraging the formal grammar of programming languages, candidates are pruned aggressively during both training and inference. This incorporation ensures only syntactically valid programs are considered, improving the efficiency and accuracy of the synthesis process.
3. Joint Syntax Learning: In scenarios where formal grammar specifications are unavailable, the architecture can also implicitly learn the syntax by employing an LSTM module that models and applies syntactic constraints jointly with program correctness constraints.

The implementation employs a neural program synthesis system for the Karel programming language, an educational grid-based language supporting constructs such as loops and conditionals.

Experimental Results

The performance evaluation is conducted on a synthetically generated dataset using the Karel DSL. The paper compares various models, including those trained purely via maximum likelihood estimation (MLE), against those enhanced with reinforcement learning and syntax learning mechanisms.

Key findings include:

• Improved Generalization: Reinforcement learning models exhibit superior generalization performance over traditional MLE models, attributed to effectively addressing program aliasing and optimizing true program synthesis objectives.
• Syntax Conditioning: Systems using syntax-based pruning, whether through inherent grammar knowledge or learned constraints, demonstrate enhanced data efficiency. Specifically, models utilizing syntax constraints significantly outperform their counterparts when trained on limited data, emphasizing the importance of syntax awareness in low-resource scenarios.
• Better Program Efficiency: The reinforcement learning model, particularly the one with a diversity objective that penalizes longer runtimes, displayed improvements in generating concise and efficient programs.

Implications and Future Directions

The proposed framework not only improves the accuracy and robustness of neural program synthesis but also sets the stage for further integration of RL methodologies with formal language aspects. The implications extend across domains where program synthesis from sparse examples is critical, including automated code generation and educational tools for programming.

Future research directions could explore the application of this framework to more complex programming languages and real-world software development tasks. The synergy of reinforcement learning and grammar-based knowledge harbors potential for advancing generalized AI capabilities in program understanding and generation fields.

Through this work, the authors contribute significantly to the evolving landscape of AI-driven program synthesis, showcasing how an interdisciplinary approach embracing both neural networks and formal linguistics can overcome traditional methodological limitations.