[2604.03189] Reflective Context Learning: Studying the Optimization Primitives of Context Space

Computer Science > Machine Learning arXiv:2604.03189 (cs) [Submitted on 3 Apr 2026] Title:Reflective Context Learning: Studying the Optimization Primitives of Context Space Authors:Nikita Vassilyev, William Berrios, Ruowang Zhang, Bo Han, Douwe Kiela, Shikib Mehri View a PDF of the paper titled Reflective Context Learning: Studying the Optimization Primitives of Context Space, by Nikita Vassilyev and 5 other authors View PDF HTML (experimental) Abstract:Generally capable agents must learn from experience in ways that generalize across tasks and environments. The fundamental problems of learning, including credit assignment, overfitting, forgetting, local optima, and high-variance learning signals, persist whether the learned object lies in parameter space or context space. While these challenges are well understood in classical machine learning optimization, they remain underexplored in context space, leading current methods to be fragmented and ad hoc. We present Reflective Context Learning (RCL), a unified framework for agents that learn through repeated interaction, reflection on behavior and failure modes, and iterative updates to context. In RCL, reflection converts trajectories and current context into a directional update signal analogous to gradients, while mutation applies that signal to improve future behavior in context space. We recast recent context-optimization approaches as instances of this shared learning problem and systematically extend them with classic...