SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
Quick Answer
The SOLAR agent introduces a self-optimizing framework for continual learning, outperforming traditional fine-tuning methods.
Quick Take
By leveraging parameter-level meta-learning and multi-level reinforcement learning, SOLAR adapts efficiently to dynamic environments, significantly enhancing performance across various reasoning tasks.
Key Points
- SOLAR autonomously discovers adaptation strategies using multi-level reinforcement learning.
- It maintains a dynamic knowledge base to balance adaptation and retention.
- Experiments show SOLAR outperforms strong baselines in reasoning tasks.
- The model is effective for transfer-learning with a strong prior on common-sense knowledge.
- SOLAR addresses challenges in deploying in non-stationary data streams.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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. Traditional fine-tuning (FT) struggles to adapt to non-stationary data streams without resulting in catastrophic for getting or requiring extensive manual data curation. To address these limitations within the streaming and continual learning paradigm, we propose the Self-Optimizing Lifelong Autonomous Reasoner (SOLAR) which is an open-ended autonomous agent that leverages parameter-level meta-learning to self-improve, treating model weights as an environment for exploration. It initiates the process by consolidating a strong prior over common-sense knowledge making it effective for transfer-learning. By utilizing a multi-level reinforcement learning approach, SOLAR autonomously discovers adaptation strategies, enabling efficient test-time adaptation to unseen domains. Crucially, SOLAR maintains an evolving knowledge base of valid modification strategies, implicitly acting as an episodic memory buffer to balance plasticity (adaptation to new tasks) and stability (retention of meta-knowledge). Experiments demonstrate that SOLAR outperforms strong baselines on common-sense, mathematical, medical, coding, social and logical reasoning tasks, marking a significant step toward autonomous agents capable of lifelong adaptation in evolving environments.
| Comments: | Accepted at "Association for the Advancement of Artificial Intelligence 2026 Conference" in Streaming Continual Learning Bridge. Published in CEUR Workshop Proceedings (Original version at this https URL) |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.20189 [cs.AI] |
| (or arXiv:2605.20189v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20189 arXiv-issued DOI via DataCite |
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| Journal reference: | CEUR Workshop Proceedings, Vol. 4183, 2026 |
Submission history
From: Nitin Vetcha [view email]
[v1]
Mon, 23 Mar 2026 07:18:02 UTC (20,314 KB)
— Originally published at arxiv.org
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