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....
Xi-Kai Yang, Hieu Trung Nguyen, Dun-Yuan Xu et al.
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