The January 6, 2026 paper introduces MEMRL, a framework designed to help AI agents master new skills by mimicking human episodic memory without needing to update the model's underlying weights. This approach addresses the stability-plasticity dilemma by decoupling a stable, frozen Large Language Model (the reasoning core) from a dynamic, evolving memory bank. Unlike standard retrieval methods that rely solely on semantic similarity, MEMRL uses non-parametric reinforcement learning to evaluate the actual utility of past experiences. It employs a two-phase retrieval mechanism that first identifies relevant candidates and then selects the most effective ones based on learned Q-values. These values are continuously refined through environmental feedback, allowing the agent to distinguish high-value strategies from distracting noise. Experiments across various benchmarks show that MEMRL significantly improves performance and supports stable runtime learning while avoiding the computational costs and forgetting associated with fine-tuning. Source: https://arxiv.org/pdf/2601.03192