SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
The paper presents SOLAR, a self-optimizing autonomous agent designed for lifelong learning and continual adaptation. It addresses challenges faced by large language models in dynamic environments, particularly concept drift and adaptation costs. Through a multi-level reinforcement learning approach, SOLAR autonomously discovers strategies for efficient adaptation while maintaining a balance between plasticity and stability.
- ▪SOLAR leverages parameter-level meta-learning to self-improve and adapt to new tasks.
- ▪It consolidates common-sense knowledge to enhance transfer-learning capabilities.
- ▪Experiments show that SOLAR outperforms strong baselines in various reasoning tasks.
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Computer Science > Artificial Intelligence arXiv:2605.20189 (cs) [Submitted on 23 Mar 2026] Title:SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation Authors:Nitin Vetcha, Dianbo Liu View a PDF of the paper titled SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation, by Nitin Vetcha and Dianbo Liu View PDF HTML (experimental) Abstract:Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying in dynamic, real-world settings with primary challenges being concept drift and the high cost of gradient-based adaptation.
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