In a collaboration between MIT, Meta FAIR, New York University on a paper published on January 27, 2026 researchers introduces SOAR, a meta-reinforcement learning framework designed to help large language models overcome learning plateaus on exceptionally difficult problems. When models fail to solve any problems in a dataset, they lack the necessary rewards to improve; SOAR addresses this by using a teacher model to generate a "stepping stone" curriculum of easier, synthetic tasks. Unlike previous methods that rely on internal metrics, the teacher is rewarded based on the student model's measurable progress on the original hard problems. The study demonstrates that a model's ability to teach is distinct from its ability to solve, as it can generate helpful guidance even for problems it cannot yet master. Furthermore, the researchers discovered that the structural quality of these generated questions is more vital for student improvement than the correctness of the provided answers. Ultimately, SOAR provides a stable and diverse path for models to self-improve without requiring additional human-curated data. Source: January 27, 2026 Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability MIT, Meta FAIR, New York University Shobhita Sundaram, John Quan, Ariel Kwiatkowski, Kartik Ahuja, Yann Ollivier, Julia Kempe https://arxiv.org/pdf/2601.18778