Self-Improving Reasoning Agents via Reinforcement Learning and Iterative Feedback
Keywords:
self-improving agents; reinforcement learning; iterative feedback; reasoning; governance; infrastructure; robustness; fairness; sustainability; human feedbackAbstract
Self-improving reasoning agents represent a class of artificial intelligence systems that refine their own problem-solving behavior through repeated cycles of reinforcement learning, evaluative feedback, and environmental interaction. Unlike static models that are deployed after a single training phase, these agents are designed to treat reasoning as an evolving capability shaped by feedback from users, tools, simulations, and governance mechanisms. This paper examines the system-level architecture, learning dynamics, infrastructure requirements, and policy implications of such agents. It argues that self-improvement is not merely an algorithmic property but a socio-technical process involving data pipelines, reward design, evaluation regimes, deployment constraints, and institutional oversight. The analysis considers reinforcement learning from human preferences, self-play, verbal reinforcement, self-refinement, and trajectory filtering as complementary mechanisms that can support iterative reasoning. It also investigates structural trade-offs among scalability, robustness, fairness, interpretability, and environmental sustainability. Particular attention is given to the risks of reward hacking, feedback loops, distributional shift, and opaque optimization. The paper concludes that durable self-improvement requires layered governance, auditable feedback channels, and infrastructure that can support continuous evaluation without sacrificing safety or equity. The discussion is relevant to researchers and practitioners building reasoning agents for domains such as software engineering, scientific discovery, education, and decision support.
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