Does Fine-tuning by Reinforcement Learning Improve Generalization in Binary Speech Deepfake Detection?
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866910039255547904 |
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| author | Wang, Xin Wanying, Ge Yamagishi, Junichi |
| author_facet | Wang, Xin Wanying, Ge Yamagishi, Junichi |
| contents | Building speech deepfake detection models that are generalizable to unseen attacks remains a challenging problem. Although the field has shifted toward a pre-training and fine-tuning paradigm using speech foundation models, most approaches rely solely on supervised fine-tuning (SFT). Inspired by the field of large language models, wherein reinforcement learning (RL) is used for model fine-tuning, we investigate the impact of RL, specifically Group Relative Policy Optimization (GRPO). The results from experiments using multiple detectors and test sets indicate that pure GRPO-based fine-tuning improves performance on out-of-domain test sets while maintaining performance on target-domain test data. This approach outperforms both SFT-only and hybrid setups. Our ablation studies further suggest that the negative reward in GRPO may be a key factor in this improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Does Fine-tuning by Reinforcement Learning Improve Generalization in Binary Speech Deepfake Detection? Wang, Xin Wanying, Ge Yamagishi, Junichi Audio and Speech Processing Building speech deepfake detection models that are generalizable to unseen attacks remains a challenging problem. Although the field has shifted toward a pre-training and fine-tuning paradigm using speech foundation models, most approaches rely solely on supervised fine-tuning (SFT). Inspired by the field of large language models, wherein reinforcement learning (RL) is used for model fine-tuning, we investigate the impact of RL, specifically Group Relative Policy Optimization (GRPO). The results from experiments using multiple detectors and test sets indicate that pure GRPO-based fine-tuning improves performance on out-of-domain test sets while maintaining performance on target-domain test data. This approach outperforms both SFT-only and hybrid setups. Our ablation studies further suggest that the negative reward in GRPO may be a key factor in this improvement. |
| title | Does Fine-tuning by Reinforcement Learning Improve Generalization in Binary Speech Deepfake Detection? |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.02914 |