MEMRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic
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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
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