CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
📰 ArXiv cs.AI
Improve code generation with CodeRL+ by aligning execution semantics using reinforcement learning and verifiable rewards
Action Steps
- Implement CodeRL+ using reinforcement learning with verifiable rewards to align execution semantics
- Train a Large Language Model (LLM) on a code corpus with RLVR to improve code generation
- Evaluate the generated code using test cases and outcome rewards
- Fine-tune the LLM using the feedback from the evaluation step
- Apply CodeRL+ to real-world code generation tasks to improve functional correctness
Who Needs to Know This
ML engineers and researchers can benefit from this approach to enhance the functional correctness of generated code, while software developers can apply these techniques to improve code quality
Key Insight
💡 CodeRL+ bridges the semantic gap between LLM training on textual patterns and functional correctness using reinforcement learning with verifiable rewards
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🚀 Improve code generation with CodeRL+! Align execution semantics using reinforcement learning and verifiable rewards 🤖
Full Article
Title: CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Abstract:
arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and the goal of functional correctness, which is governed by formal execution semantics. Reinforcement Learning with Verifiable Rewards (RLVR) approaches attempt to bridge this gap using outcome rewards from executing test cases. However, solely relying on binary pass
Abstract:
arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and the goal of functional correctness, which is governed by formal execution semantics. Reinforcement Learning with Verifiable Rewards (RLVR) approaches attempt to bridge this gap using outcome rewards from executing test cases. However, solely relying on binary pass
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