Evaluation and Restoration of Safety in Reasoning-Tuned LLMs

Open-source LLMs that are optimized for reasoning, such as VibeThinker, aim to democratize AI by bringing high-capability models to users with limited resources. They are broadly available but lack regulation and external guardrails; therefore, in order to integrate them safely into human society, it is critical that they perform in a safe manner. There are popular and intuitive arguments for why improved reasoning should improve safety, but recent evidence suggests that, in fact, the opposite may be true. This paper aims to characterize and mitigate this issue. First, it develops a metric for evaluating LLMs optimized for reasoning, and shows that increasing reasoning across multiple model families increases the unsafe rate in all cases. Second, it proposes a method for restoring safety by combining evolution strategies with a post-training guardrail (ESPG). Compared to alternative methods for safety post-training such as GRPO, ESPG is simple to implement, requires no GPU memory beyond that of inference, and works with remarkably few training samples (e.g. 30), yet makes the model significantly safer. The study thus establishes a path towards safe post-training of open-source AI models.