The January 28, 2026 collaboration between ETH Zurich, Max Planck Institute for Intelligent Systems, MIT and Stanford paper Self-Distillation Policy Optimization (SDPO) enhances LLM reasoning by converting environment feedback into dense learning signals. Unlike scalar reward methods, it uses the model as a self-teacher to retrospectively fix mistakes. This improves sample efficiency and accuracy at scale. Source: https://arxiv.org/pdf/2601.20802 Title: Reinforcement Learning via Self-Distillation January 28, 2026. Institutions: * ETH Zurich * Max Planck Institute for Intelligent Systems * MIT * Stanford Authors: * Jonas Hubotter (ETH Zurich) * Frederike Lubeck (ETH Zurich, Max Planck Institute for Intelligent Systems) * Lejs Behric (ETH Zurich) * Anton Baumann (ETH Zurich) * Marco Bagatella (ETH Zurich, Max Planck Institute for Intelligent Systems) * Daniel Marta (ETH Zurich) * Ido Hakimi (ETH Zurich) * Idan Shenfeld (MIT) * Thomas Kleine Buening (ETH Zurich) * Carlos Guestrin (Stanford) * Andreas Krause (ETH Zurich)