Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus
Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly external resources such as powerful teacher models or reliable test units. However, in real-world scenarios, it is much harder to obtain reference solutions and test oracles than problem descriptions and test inputs. In this paper, we tackle a challenging yet realistic question: Can a code language model improve itself without access to a superior teacher and a test oracle? To answer this, we propose ConSelf, a self-improving approach built upon two key ideas. First, we introduce code semantic entropy, a novel metric...
| Authors | Huan Zhang, Wei Cheng, Wei Hu |
|---|---|
| Primary category | cs.SE |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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