ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization
Large Language Models (LLMs) excel at code generation but remain heavily reliant on large-scale annotated solutions and verification-based supervision, which constrains scalability and hinders sustained self-improvement. Recent solver--verifier frameworks exploit program execution as an automatic supervision signal, but their effectiveness degrades as solvers become moderately strong: verifier-generated tests increasingly confirm semantic correctness rather than exposing the remaining failure modes. We propose \textbf{ACE}, a self-evolving code generation framework based on a solver--adversary architecture that prioritizes active failure discovery through execution-centric supervision. A ...
| Authors | Yixu Huang, Xinglei Yu, Zhongyu Wei |
|---|---|
| Primary category | cs.SE |
| Published | 17 April 2026 |
| Last updated at source | 21 May 2026 |
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