arXiv Machine Learning June 16, 2026 By Jiangze Yan, Yi Shen, Wenjing Zhang, Jieyun Huang, Zhaoxiang Liu, Ning Wang, Kai Wang, Shiguo Lian

HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents

Read the original on arXiv Machine Learning →

arXiv:2606. 16285v1 Announce Type: cross Abstract: Long-horizon agents rely on memory mechanisms to compress interaction history, but optimizing memory writing faces a distinct credit assignment challenge: a memory update may be rewarded or penalized due to downstream tool failures, noisy observations, or reasoning errors rather than its own contribution.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers

Jul 9

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed.

llms agents fine-tuning

arXiv AI

Jul 10

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

arXiv:2607. 08716v1 Announce Type: new Abstract: In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act.

By Yifan Wu, Lizhu Zhang, Yuhang Zhou, Mingyi Wang, Bo Peng, Serena Li, Xiangjun Fan, Zhuokai Zhao

llms agents fine-tuning

arXiv AI

Aug 3

AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction

arXiv:2607. 21106v2 Announce Type: replace Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging.

By Qinfeng Li, Yuntai Bao, Xinyan Yu, Hongze Chen, Yanmin Liu, Wenqi Zhang, Xuhong Zhang

llms agents nlp reinforcement-learning benchmarks

arXiv AI

Jul 24

AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning

arXiv:2607. 21106v1 Announce Type: new Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging.

By Qinfeng Li, Yuntai Bao, Xinyan Yu, Hongze Chen, Wenqi Zhang, Xuhong Zhang

llms agents nlp reinforcement-learning benchmarks

arXiv Machine Learning

Aug 6

Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

arXiv:2608. 05111v1 Announce Type: new Abstract: In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies.

By Jai Malegaonkar, Rohan Patil, Henrik I. Christensen

agents reinforcement-learning efficiency

arXiv Machine Learning

Aug 4

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.

By Yi Yang, Zhennan Chen, Yihong Zhuang, Tiehan Fan, Yinan Chen, Jian Li, Jian Yang, Ying Tai

llms agents reinforcement-learning

Related stories

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction

AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning

Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States