AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916514360197120 |
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| author | Aggarwal, Pranjal Parno, Bryan Welleck, Sean |
| author_facet | Aggarwal, Pranjal Parno, Bryan Welleck, Sean |
| contents | Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal verification to provide mathematical guarantees that the generated code is correct. However, generating formally verified code with LLMs is hindered by the scarcity of training data and the complexity of formal proofs. To tackle this challenge, we introduce AlphaVerus, a self-improving framework that bootstraps formally verified code generation by iteratively translating programs from a higher-resource language and leveraging feedback from a verifier. AlphaVerus operates in three phases: exploration of candidate translations, Treefinement -- a novel tree search algorithm for program refinement using verifier feedback, and filtering misaligned specifications and programs to prevent reward hacking. Through this iterative process, AlphaVerus enables a LLaMA-3.1-70B model to generate verified code without human intervention or model finetuning. AlphaVerus shows an ability to generate formally verified solutions for HumanEval and MBPP, laying the groundwork for truly trustworthy code-generation agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement Aggarwal, Pranjal Parno, Bryan Welleck, Sean Machine Learning Artificial Intelligence Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal verification to provide mathematical guarantees that the generated code is correct. However, generating formally verified code with LLMs is hindered by the scarcity of training data and the complexity of formal proofs. To tackle this challenge, we introduce AlphaVerus, a self-improving framework that bootstraps formally verified code generation by iteratively translating programs from a higher-resource language and leveraging feedback from a verifier. AlphaVerus operates in three phases: exploration of candidate translations, Treefinement -- a novel tree search algorithm for program refinement using verifier feedback, and filtering misaligned specifications and programs to prevent reward hacking. Through this iterative process, AlphaVerus enables a LLaMA-3.1-70B model to generate verified code without human intervention or model finetuning. AlphaVerus shows an ability to generate formally verified solutions for HumanEval and MBPP, laying the groundwork for truly trustworthy code-generation agents. |
| title | AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06176 |