Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation
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Computer Science > Computation and Language
arXiv:2609.26536 (cs)
[Submitted on 22 Sep 2026]
Title:Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation
View a PDF of the paper titled Transcribe, Translate, and Optimize: Joint Reward Learning for Speech Translation, by Yanghe Dong and 2 other authors
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Abstract:In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fine-tuning (SFT) and model-generated transcripts at inference. To address this, we propose joint recognition and translation fine-tuning via group relative policy optimization (GRPO). We score both transcripts and translations, with translation conditioned on model-generated transcripts, and compare three token advantage strategies. Using Qwen2.5-Omni-3B across four languages, we evaluate CoT against direct speech translation (Direct ST) under SFT and GRPO, training on CoVoST 2 and testing on CoVoST 2 and FLEURS. CoT GRPO outperforms Direct ST GRPO by 1.77 and 0.83 average BLEU points on CoVoST 2 and FLEURS. Compared to CoT SFT, GRPO boosts BLEU by 0.82 and 0.67 points and reduces word error rate (WER) by 8.8% and 7.2% relatively. These results highlight reinforcement fine-tuning as an effective method to mitigate the training-inference mismatch, jointly improving recognition and translation.
| Comments: | 5 pages |
| Subjects: | Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2609.26536 [cs.CL] |
| (or arXiv:2609.26536v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26536
arXiv-issued DOI via DataCite (pending registration)
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