RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments
Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor, and verifier agents to continually explore the environment, validate
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Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor, and verifier agents to continually explore the environment, validate
This work introduces a novel multi-agent framework for autonomous self-improvement, which is crucial for developing robust and adaptive AI systems in dynamic environments. ML engineers can leverage these concepts to build agents that learn and adapt without constant human intervention, reducing maintenance overhead and expanding applicability.