Memory-Augmented RL

RL: Reinforcement Learning

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Latest papers

12 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Remember Less, Adapt Longer: Bounded-State In-Context Reinforcement Learning with Recurrent Summary Memory
Oct 19, 2026Jiawen Wang, Cheng Xue, Peng Zhang +1Reinforcement Learning In-Context RL
The Australian National University
Value as Memory: Online Value-Guided Post-training for General Robot Policies
Oct 15, 2026Wenye Yu, Yijin Chen, Kaiyi Xiong +7Robotic RL RL Post-Training
Shanghai Jiao Tong University · Shanghai Innovation Institute · Noematrix Ltd.
All-in-One Memory: Process-Rewarded Reinforcement Learning for Memory Agents
Oct 15, 2026Bingbing Wang, Jingjie Lin, Weiming Qiao +2Persistent Memory for Language Models Memory-Augmented Language Model Agents
Harbin Institute of Technology, Shenzhen · The Hong Kong Polytechnic University · Fudan University +1
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
Oct 13, 2026Yousef Liang, Ryan Ardalan, Yarden Goraly +6Memory-Augmented RL Robot Navigation
Stanford University · University of California, Berkeley · NVIDIA
ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
Sep 30, 2026Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1RL Benchmarks Reinforcement Learning
Innopolis University, Innopolis, Russia · MIRIAI, Moscow, Russia · Cognitive AI Systems Lab, Moscow, Russia
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Jul 6, 2026Muhammad Zain Amin, Kibele Sebnem YildirimReinforcement Learning RL for Language Model Reasoning
Department of Software Engineering and IT, ´Ecole de Technologie Sup´erieure, Montreal, Canada · Centre int´egr´e de traumatologie, Hˆopital du Sacr´e-Cœur de Montr´eal, Universit´e de Montr´eal, Montreal, Canada · Desautels Faculty of Management, McGill University, Montreal, Canada.
CORE Planner: Contextual-memory Oriented Reinforcement-learning in Unknown Environments for Robot Navigation
Jun 28, 2026Jintao Kong, Zhihao Zhang, Weihuang Chen +4Reinforcement Learning Robot Navigation
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
May 18, 2026Peilin Wu, Xinlu Zhang, Kun Wan +4RL for Language Models Reinforcement Learning
The University of Texas at Dallas · Department of Computer Science, University of California, Santa Barbara · Adobe Inc.
CaMeRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments
May 14, 2026Hong Hong, Feiyu Liao, Yongheng Liang +3RL for Robotics Reinforcement Learning
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China, and the Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Writer-R1: Dynamic Rubric Mining for Reinforcement Learning of Open-Ended Writing
Mar 16, 2026Jihao Zhao, Shuaishuai Zu, Zhiyuan Ji +2Memory-Augmented RL RL for Language Models
School of Information, Renmin University of China, Beijing, China · School of Information, BRAIN, Renmin University of China, Beijing, China
Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
Feb 20, 2026Narjes Nourzad, Carlee Joe-WongMemory-Augmented RL Reinforcement Learning
University of Southern California · Carnegie Mellon University
MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
Feb 20, 2026Narjes Nourzad, Carlee Joe-WongMemory-Augmented RL Reinforcement Learning
University of Southern California, Electrical and Computer Engineering, Los Angeles, CA 90007, USA · Carnegie Mellon University, Electrical and Computer Engineering, Pittsburgh, PA 15213, USA

Oct 19, 2026J/K move · Enter open · S save

Remember Less, Adapt Longer: Bounded-State In-Context Reinforcement Learning with Recurrent Summary Memory

Jiawen Wang, Cheng Xue, Peng Zhang, Jochen Renz

The Australian National University

Reinforcement Learning In-Context RL Memory-Augmented RL Transformer-Based RL Continual RL

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In-context reinforcement learning (ICRL) enables agents to adapt to new tasks without parameter updates, relying on memory to retain what they learn from experience. For agents operating continually, existing approaches using memory trade off bounded cost against access to past experience. Approaches using growing memories, such as full-history attention and accumulating summaries, incur storage and per-decision costs that increase with task length. Fixed-size memories bound storage but restrict access: sliding windows discard older steps, while recurrent networks compress even recent interactions into a hidden state. We introduce Recurrent Summary Memory (RSM), a Transformer policy that attends exactly to a short working buffer of recent interactions and carries a fixed-capacity summary of earlier experience. When the buffer fills, RSM rewrites the summary from its previous contents and the buffer, then clears the buffer, so deployment memory and per-decision cost stay

O(1)

in task length. On long-running partially observable tasks, frozen RSM policies sustain their adapted performance for eight times the training horizon with about 1% of full-history memory, ending ahead of every evaluated baseline while growing memories deteriorate. When the task configuration changes mid-run, RSM re-adapts better than growing and recurrent memories. Clearing the summary or blocking the policy's access to it removes its benefit, while swapping in another task's summary steers behaviour towards that task. Together, these results show that our fixed-capacity summary can sustain in-context adaptation far beyond the training horizon.