arXiv AI By Wenzhe Fan, Benjamin Steenhoek, Gaurav Mittal, Yu Hu

Holding a Steady Course: Knowledge Evolution from Expertise and Lessons for Continual Agent Learning

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arXiv AI
Sep 23

ACLArena: Agent Continue Learning in Multi-stage Post-training

arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...

By Haixin Wang, Xiaoxuan Wang, Junkai Zhang, Han Zhang, Renliang Sun, Alexander K Taylor, Yidan Shi, Haoran Deng, Chenguang Wang, Jason Cong, Yizhou Sun, Wei Wang
arXiv AI
Oct 16

Reliable Knowledge Acquisition for Continual Learning Agents

The paper introduces RIKA, a method for reliable knowledge acquisition in continual learning agents. RIKA hierarchically extracts factual, conceptual, procedural, and metacognitive knowledge and uses evidence-based admission to decide which claims are incorporated into the agent’s context, avoiding the reuse of unreliable information. Experiments on six task streams and four model backbones show that RIKA outperforms existing memory and experience-learning systems on aggregate performance.

By Yixu Huang, Jiaqi W. Ma
arXiv AI
Oct 7

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

Learn2Play Bench is a new benchmark that tests how well large language model agents learn from experience in unfamiliar, text‑based games with novel or counterintuitive rules. The benchmark provides reproducible feedback, automatic scoring, and varied game instances to evaluate learning across repeated attempts and transfer to new situations. Findings show that retaining full action records aids learning, human players outperform agents, and the choice of harness significantly impacts performance and inference cost.

By Yibo Li, Jinhang Qiu, Zhi Zheng, Qianyun Guo, Jiaying Wu, Shuo Ji, Bryan Hooi