RRSI: Regularized Recursive Self-Improvement of Agent Harnesses
An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically establishing a form of recursive self-improvement (RSI) at th
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An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically establishing a form of recursive self-improvement (RSI) at th
This paper addresses a critical challenge in LLM agent development: preventing overfitting during self-improvement. For ML engineers, understanding regularization techniques in agent harnesses is crucial for building robust and generalizable AI systems that perform well on unseen tasks, enhancing the reliability and deployment readiness of agents.