SOLAR: Self-Optimizing LLM Agent for Lifelong Learning
Researchers propose SOLAR (Self-Optimizing Lifelong Autonomous Reasoner), an open-ended autonomous agent designed to overcome key limitations of large language models (LLMs) in dynamic, real-world settings. Traditional fine-tuning struggles with concept drift and catastrophic forgetting in non-stationary data streams, requiring extensive manual curation. SOLAR leverages parameter-level meta-learning to treat model weights as an environment for exploration, enabling self-improvement without gradient-based adaptation. It consolidates a strong prior over common-sense knowledge for effective transfer learning and uses multi-level reinforcement learning to autonomously discover adaptation strategies. The approach aims to enable continual adaptation in streaming data environments, addressing the high cost of retraining and the need for manual data curation. The paper is published on arXiv under identifier 2605.20189.
Key facts
- SOLAR stands for Self-Optimizing Lifelong Autonomous Reasoner.
- It addresses concept drift and catastrophic forgetting in LLMs.
- Uses parameter-level meta-learning for self-improvement.
- Treats model weights as an environment for exploration.
- Consolidates common-sense knowledge prior for transfer learning.
- Employs multi-level reinforcement learning for adaptation strategies.
- Aims to reduce high cost of gradient-based adaptation.
- Published on arXiv with ID 2605.20189.
Entities
Institutions
- arXiv