Reinforcement Learning for Hallucination-Aware Reasoning and Factuality Calibration in Large Language Models
Keywords:
reinforcement learning, hallucination, factuality calibration, large language models, AI governance, uncertainty estimation, retrieval augmentation, socio-technical systemsAbstract
Large language models have become general-purpose reasoning and communication infrastructures, yet their fluency frequently outpaces their factual reliability. Hallucination is not merely a model error but a socio-technical failure produced by data curation, objective design, decoding behavior, evaluation practice, deployment incentives, and governance gaps. This paper examines reinforcement learning as a mechanism for hallucination-aware reasoning and factuality calibration. It argues that reinforcement learning must be embedded within a layered architecture that combines uncertainty estimation, retrieval grounding, verifier networks, process supervision, human oversight, auditability, and deployment safeguards. The analysis emphasizes structural trade-offs among factuality, helpfulness, latency, computational cost, transparency, and fairness. It also considers how reward design can induce reward hacking, over-abstention, stylistic manipulation, and hidden technical debt. Through cross-domain examples in healthcare, law, journalism, and public administration, the paper shows that factuality calibration cannot be reduced to a single benchmark or training objective. Instead, it requires continuous monitoring, model documentation, incident response, red teaming, and policy mechanisms that assign responsibility across model developers, deployers, auditors, and users. The paper concludes that hallucination-aware reinforcement learning should be treated as an infrastructure problem: sustainable, robust, contestable, and accountable by design.
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