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

• URL: http://arxiv.org/abs/2605.20189v1
• Date: Mon, 23 Mar 2026 07:18:02 GMT
• Title: SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
• Abstract summary: Self-Optimizing Lifelong Autonomous Reasoner (SOLAR) is an open-ended autonomous agent.<n>It uses parameter-level meta-learning to self-improve, treating model weights as an environment for exploration.<n> Experiments demonstrate that SOLAR outperforms strong baselines on common-sense, mathematical, medical, coding, social and logical reasoning tasks.
• Score: 3.239706565815636
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

Related papers

• Self-evolving LLM agents with in-distribution Optimization [60.05867547965365]
  Large Language Models (LLMs) have recently emerged as powerful controllers for interactive agents in complex environments.<n>We propose Q-Evolve, a self-evolving framework for LLM agents that unifies automatic process-reward labeling and policy learning.<n>We evaluate our method on AlfWorld, WebShop, and ScienceWorld, showing Q-Evolve outperforms strong baselines in sample efficiency, robustness, and overall task performance.
  arXiv  Detail & Related papers  (2026-06-05T15:09:52Z)
• DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models [63.58458696122293]
  Continual learning enables vision-language models to accumulate knowledge and adapt to evolving tasks without retraining from scratch.<n>Most existing methods rely on fixed architectures with statically allocated parameters, which limits adaptation to new domains and aggravates catastrophic forgetting.<n>We propose DIMoE-Adapters, a Dynamic Incremental Mixture-of-Experts Adapters framework that introduces a dynamic expert evolution paradigm to balance stability and plasticity.
  arXiv  Detail & Related papers  (2026-05-08T09:32:05Z)
• Position: Agentic Evolution is the Path to Evolving LLMs [56.733933092220845]
  We argue that addressing this limitation requires a new scaling axis-evolution.<n>Existing deployment-time adaptation methods lack the strategic agency needed to diagnose failures and produce durable improvements.
  arXiv  Detail & Related papers  (2026-01-30T22:15:58Z)
• Aligning Agentic World Models via Knowledgeable Experience Learning [68.85843641222186]
  We introduce WorldMind, a framework that constructs a symbolic World Knowledge Repository by synthesizing environmental feedback.<n>WorldMind achieves superior performance compared to baselines with remarkable cross-model and cross-environment transferability.
  arXiv  Detail & Related papers  (2026-01-19T17:33:31Z)
• Emotion-Inspired Learning Signals (EILS): A Homeostatic Framework for Adaptive Autonomous Agents [0.0]
  This paper proposes that the unaddressed factor in robust autonomy is a functional analog to biological emotion, serving as a high-level homeostatic control mechanism.<n>We introduce Emotion-Inspired Learning Signals (EILS), a unified framework that replaces scattered optimizations with a coherent, bio-inspired internal feedback engine.
  arXiv  Detail & Related papers  (2025-12-20T19:46:51Z)
• POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems? [0.0]
  POLARIS is a three-layer multi-agentic self-adaptation framework.<n>It handles uncertainty, learns from past actions, and evolves its strategies.<n>Preliminary evaluation on two self-adaptive exemplars, SWIM and SWITCH, shows that POLARIS consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-12-04T11:51:03Z)
• Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers [51.992454203752686]
  Transformer models learn in two distinct modes: in-weights learning (IWL) and in-context learning (ICL)<n>We draw inspiration from evolutionary biology's analogous adaptive strategies: genetic encoding and phenotypic plasticity.<n>We experimentally operationalize these dimensions of predictability and investigate their influence on the ICL/IWL balance in Transformers.
  arXiv  Detail & Related papers  (2025-05-14T23:31:17Z)
• Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving [9.515695438588855]
  High-speed off-road autonomous driving presents unique challenges due to complex, evolving terrain characteristics.<n>We propose a novel framework that combines a Kalman filter-based online adaptation scheme with meta-learned parameters to address these challenges.<n>Our results underscore the effectiveness of meta-learned dynamics model adaptation, advancing the development of reliable autonomous systems.
  arXiv  Detail & Related papers  (2025-04-23T17:51:36Z)
• The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry [4.704582238028159]
  We investigate the impact of catastrophic forgetting and the effectiveness of knowledge transfer in neural networks trained continuously in an embodied setting. We observe initial satisfactory performance with high transferability between environments, followed by a specialization phase. These findings emphasize the open challenges of balancing adaptation and memory retention in lifelong robotics.
  arXiv  Detail & Related papers  (2024-06-03T21:32:50Z)
• SELF: Self-Evolution with Language Feedback [68.6673019284853]
  'SELF' (Self-Evolution with Language Feedback) is a novel approach to advance large language models. It enables LLMs to self-improve through self-reflection, akin to human learning processes. Our experiments in mathematics and general tasks demonstrate that SELF can enhance the capabilities of LLMs without human intervention.
  arXiv  Detail & Related papers  (2023-10-01T00:52:24Z)
• Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence [59.11038175596807]
  Continual learning aims to empower artificial intelligence with strong adaptability to the real world. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting. We propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity.
  arXiv  Detail & Related papers  (2023-08-29T02:43:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.