Reward-Guided Text Generation for Automated Scientific Question Answering
Keywords:
reward-guided text generation; scientific question answering; large language models; reinforcement learning from human feedback; retrieval-augmented generation; governance; sustainable AIAbstract
Reward-guided text generation has emerged as a promising framework for improving automated scientific question answering systems in which answers must be accurate, grounded, and appropriately calibrated. This paper presents a system-level analysis of reward-guided generation for scientific queries, focusing on architectural trade-offs, reward model design, infrastructure requirements, deployment constraints, robustness, fairness, and governance. Rather than proposing a single algorithm, the paper examines how reward signals can be operationalized across retrieval, candidate generation, evaluation, and policy updating to support reliable scientific answer production. It discusses trajectory-level reasoning path filtering as a scalable strategy, the role of retriever-augmented architectures, and the challenge of reward model overoptimization. The analysis further addresses verification infrastructure, calibration, safety, and the socio-technical implications of deploying such systems in scholarly environments. Attention is given to organizational model updates, auditability, and sustainability. The paper argues that reward-guided scientific question answering must be treated as an evolving infrastructure rather than a static model, with explicit governance mechanisms to manage corpus drift, reward hacking, and unequal access to computational resources. It concludes by identifying policy directions for accountable deployment in academic and research settings.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
