This December 2025 research introduces Contextual Sample Efficiency (CSE), a novel algorithm designed to improve zero-shot generalization in reinforcement learning using minimal training data. Standard methods often require expensive, diverse simulations to prepare agents for new environments, but this approach leverages linear approximations of environmental dynamics to bypass extensive sampling. By incorporating gradient information regarding how rewards and transitions change with different parameters, the authors enable agents to adapt to unseen scenarios without direct experience. Their mathematical framework, based on Contextual Bellman Equations, provides a formal proof that these linear estimates can effectively bound performance errors in varying contexts. Testing across diverse MuJoCo and robotics simulations demonstrates that CSE consistently outperforms traditional baselines and matches complex existing methods. Ultimately, the study offers a scalable, computationally efficient strategy for deploying robust agents in real-world systems where environmental conditions are unpredictable. Source: December 2025 Zero-Shot Context Generalization in Reinforcement Learning from Few Training Contexts University of California, Los Angeles James Chapman, Kedar Karhadkar, Guido Montúfar https://arxiv.org/pdf/2507.07348