We present InvThink, a simple yet powerful approach that gives language models the capability of inverse thinking: reasoning through failure modes before generating responses. Unlike existing safety alignment methods that optimize directly for safe response, InvThink instructs models to 1) enumerate potential harms, 2) analyze their consequences, and 3) generate safe outputs that proactively avoid these risks. Our paper reveals three key findings: (i) InvThink demonstrates significantly improved safety reasoning as model size scales, compared to existing safety methods. (ii) InvThink mitigates safety tax; by training models to systematically consider failure modes, it preserves general reasoning capabilities on standard benchmarks. (iii) beyond general safety tasks, InvThink excels in high-stakes domains including external-facing applications (medicine, finance, law) and agentic risk scenarios (blackmail, murder), achieving up to 17.8% reduction in harmful responses compared to baseline methods like SafetyPrompt. We further equip InvThink with supervised fine-tuning, and reinforcement learning across three LLM families. These results suggest that InvThink provides a scalable and generalizable path toward safer, more capable language models.
Categories: cs.AI cs.CL