[2510.07151] ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems

Computer Science > Machine Learning arXiv:2510.07151 (cs) [Submitted on 8 Oct 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems Authors:Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov View a PDF of the paper titled ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems, by Egor Cherepanov and 2 other authors View PDF HTML (experimental) Abstract:Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches rely solely on instantaneous information, without incorporating insights from the past. Standard recurrent or transformer models struggle with retaining and leveraging long-term dependencies: context windows truncate history, while naive memory extensions fail under scale and sparsity. We propose ELMUR (External Layer Memory with Update/Rewrite), a transformer architecture with structured external memory. Each layer maintains memory embeddings, interacts with them via bidirectional cross-attention, and updates them through an Least Recently Used (LRU) memory module using replacement or convex blending. ELMUR extends effective horizons up to 100,000 times beyond the attention window and achieves a 100% success rate on a synthetic T-Maze task with corridors up to one million steps. In POPGym, it outperforms baselines on more than half of t...