Deep Reinforcement Learning for Programming Language Correction
Novice programmers often struggle with the formal syntax of programming languages. To assist them, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without any knowledge of the formal syntax of the programming language. We leverage expert demonstrations for one tenth of the training data to accelerate training. The proposed technique is evaluated on 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 14% more programs and 29% more compiler error messages relative to those fixed by a state-of-the-art tool, DeepFix, which uses a fully supervised neural machine translation approach.
Code (1)
Tasks
Deep Reinforcement LearningMachine TranslationProgram Repairreinforcement-learningReinforcement LearningReinforcement Learning (RL)TranslationSimilar Papers 제목 키워드 기반
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. Evaluating the programming capabiliti…
Code GenerationMultiple-choiceLanguage-Conditioned Reinforcement Learning to Solve Misunderstandings with Action Corrections
Human-to-human conversation is not just talking and listening. It is an incremental process where participants continually establish a common understanding to rule out misunderstandings. Current language understanding me…
Instruction Followingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Text-to-SQL Error Correction with Language Models of Code
Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing…
SQL ParsingText to SQLText-To-SQLSynthetic Error Injection Fails to Elicit Self-Correction In Language Models
Reinforcement learning has become the dominant paradigm for eliciting reasoning and self-correction capabilities in large language models, but its computational expense motivates exploration of alternatives. Inspired by …
Reinforcement LearningAutonomous Driving