The HIPIF framework enhances long-horizon learning for Large Language Models by organizing tasks around subgoals and summarizing completed histories to mitigate long-context interference. This approach improves agent performance in complex tasks without the need for auxiliary models or expert guidance, as demonstrated by successful experiments on benchmark tasks.