Tufa Labs has introduced a new framework called LADDER (Learning through Autonomous Difficulty-Driven Example Recursion) aimed at enhancing the capabilities of Large Language Models (LLMs) in mathematical problem-solving. This innovative approach allows LLMs to recursively generate and solve progressively simpler variants of complex problems, leading to a reported accuracy of up to 90% on challenging tests without the need for human feedback. The framework enables autonomous difficulty-driven learning, allowing the models to create easier problem variants and improve their problem-solving abilities. Additionally, a 7 billion parameter AI model utilizing the LADDER framework has demonstrated superior performance, achieving a score of 90% on the MIT Integration Bee, surpassing OpenAI's model, which scored 80%. The research highlights the potential of LADDER to revolutionize LLM training and performance through self-guided simplification and reinforcement learning techniques.