HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation
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Computer Science > Computation and Language
arXiv:2610.03063 (cs)
[Submitted on 2 Oct 2026]
Title:HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation
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Abstract:Large Language Models (LLMs) are prone to generating hallucinated content, which compromises their reliability in knowledge-intensive tasks. To address this challenge without sacrificing creativity, we propose HARPO, a reinforcement learning framework designed to jointly optimize faithfulness and creativity. HARPO incorporates a Hallucination-Aware Generative Reward Model (HA-GRM), trained via verifiable feedback, to assess both faithfulness and writing quality. A Selective Activation Mechanism (SAM) activates writing rewards only for outputs judged hallucination-free by HA-GRM, while a data curriculum progressively shifts training from creative writing to hallucination-centric tasks. On RAGTruth, our Qwen3-4B-based HA-GRM achieves a response-level F1 score of 78.08%, compared with 66.37% for the supervised fine-tuning baseline. Experiments on Qwen2.5 and Qwen3 models from 1.7B to 8B parameters show improvements in both faithful generation and writing quality. On Qwen3-4B, HARPO reduces the HA-GRM-judged hallucination rate on MultiHopRAG from 3.29% to 1.02%, while increasing the Arena-Hard-v2.0 creative-writing score from 16.95% to 27.54%.
| Comments: | 11 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.03063 [cs.CL] |
| (or arXiv:2610.03063v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03063
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation, by Tiezheng Yu and 6 other authors
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