Noisy Test-Time Reinforcement Learning for Code LLMs
Large language models (LLMs) have demonstrated remarkable performance across various code-related tasks. However, unlike carefully curated datasets that are typically high-quality and error-free, real-world user instructions are often vague and error-prone, posing significant challenges to the robustness of code LLMs. Furthermore, robustness-oriented fine-tuning relies on paired clean-noisy samples, which are costly to curate and require sophisticated noisy simulation techniques. To address these challenges, we propose the Noisy Test-time Reinforcement Learning framework (NTRL-Code), which enables robust self-evolution of code LLMs using only unlabeled noisy data during the testing stage....
| Authors | Xikai Yang, Hieu Trung Nguyen, Dunyuan Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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