SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation

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.

한 줄 요약

**[Lifelong Learning Agent / Meta-Learning]** SOLAR가 parameter-level meta-learning과 multi-level RL로 모델 가중치 자체를 탐색 환경으로 다뤄 self-improve, evolving knowledge base를 episodic memory로 활용해 plasticity·stability 균형.

핵심 기여도

LLM의 remarkable 성공에도 dynamic real-world 환경 배포 시 주요 도전이 concept drift와 gradient 기반 적응의 high cost임을 지적.
전통 fine-tuning이 non-stationary data stream에 catastrophic forgetting 없이 적응 불가하거나 extensive manual data curation 요구.
SOLAR(Self-Optimizing Lifelong Autonomous Reasoner) 제안 — parameter-level meta-learning으로 self-improve하는 open-ended autonomous agent, 모델 가중치를 탐색 환경으로 취급. 강한 common-sense prior consolidate로 transfer-learning 효과 확보.
Multi-level RL로 적응 전략을 autonomous 발견 — unseen domain에 효율적 test-time adaptation. 유효 modification strategy의 evolving knowledge base 유지 — episodic memory buffer 역할로 plasticity(new task 적응)과 stability(meta-knowledge 유지) 균형.

핵심 아이디어

Lifelong adaptation에는 weight 자체를 RL이 탐색하는 환경으로 보고, 유효 modification strategy를 episodic memory에 누적하는 meta-learning이 효과적이며, 이로써 gradient FT의 cost와 catastrophic forgetting을 동시 회피하면서 plasticity·stability 균형이 가능하다.

기술적 접근법

**방법론**: SOLAR — parameter-level meta-learning + multi-level RL + evolving strategy knowledge base.
**핵심 기법**: (1) 모델 가중치를 RL 탐색 환경으로 정의, (2) Common-sense knowledge prior consolidate로 transfer 시작점 강화, (3) Multi-level RL로 적응 전략 autonomous 발견, (4) Evolving knowledge base가 유효 strategy를 episodic memory처럼 누적, (5) Plasticity·stability 균형으로 catastrophic forgetting 회피.

주요 결과

Common-sense·mathematical·medical·coding·social·logical reasoning task에서 strong baseline outperform.
자율 lifelong adaptation의 가능성을 다양 도메인에서 입증.
Episodic memory 활용으로 stability·plasticity 동시 달성.

의의 및 한계

**의의**: lifelong LLM agent 개발에 weight-as-environment·meta-learning·RL의 통합 frame 제시.

실용적 활용

Dynamic 환경의 lifelong LLM agent.
Concept drift 대응 자율 적응 시스템.
Test-time adaptation·meta-learning 인프라.