Curriculum Reinforcement Learning for Progressive Complex Reasoning in Large Language Models

Abstract

Large language models have demonstrated remarkable capacity for pattern completion, instruction following, and multi-step reasoning, yet their ability to solve progressively complex tasks remains uneven, brittle, and dependent on hidden interactions among data, reward design, compute allocation, and deployment context. This paper examines curriculum reinforcement learning as a system-level strategy for cultivating progressive complex reasoning in large language models. It argues that reasoning capability is not merely an emergent property of parameter scale but an outcome of orchestrated task sequencing, feedback governance, policy optimization, verification, observability, and socio-technical safeguards. The discussion integrates curriculum learning, reinforcement learning from human feedback, chain-of-thought prompting, preference optimization, and scalable policy improvement into a layered architecture. It analyzes structural trade-offs among sample efficiency, training stability, interpretability, cost, latency, fairness, and sustainability. The paper then considers infrastructure requirements, data provenance, annotation labor, synthetic data risk, red-teaming, monitoring, incident response, and policy implications. Case illustrations from education, scientific discovery, public-sector decision support, and enterprise assistance show that staged reasoning curricula can improve robustness and transfer, but they can also amplify dependency on reward models, obscure accountability, and shift labor burdens. The conclusion calls for auditable curricula, participatory governance, lifecycle evaluation, and sustainability-aware deployment as foundational requirements for trustworthy progressive reasoning systems.