According to Anthropic (@AnthropicAI), the SGTM (Stochastic Gradient Targeted Masking) unlearning method is significantly more resilient than previous approaches. Specifically, it requires seven times more fine-tuning steps to recover forgotten knowledge using SGTM compared to the RMU (Random Masking Unlearning) method. This finding highlights a critical advancement for AI model safety and confidential data retention, as SGTM makes it much harder to reintroduce sensitive or unwanted knowledge once it has been unlearned. For enterprises and developers, this strengthens compliance and data privacy opportunities, making SGTM a promising tool for robust AI regulation and long-term security (source: Anthropic, Twitter, Dec 9, 2025).