On-Policy Delta Distillation for Multilingual Math Reasoning
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Computer Science > Computation and Language
Title:On-Policy Delta Distillation for Multilingual Math Reasoning
Abstract:On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexplored. We study OPD and its advanced variant, On-Policy Delta Distillation (OPD$^2$), for mathematical reasoning in English, Korean, and Japanese. OPD$^2$ improves OPD by using the probability gap between a post-trained teacher and its base model as the learning signal. Experiments with Qwen3 show that OPD$^2$ consistently outperforms the original OPD, with particularly strong improvements in Korean and Japanese, and generally narrows the English-Korean performance gap. We further find that English-only OPD can also increase performance for Korean and Japanese, but often shifts the responses toward English, highlighting the importance of multilingual data to preserving target-language responses.
| Comments: | 9 pages, 3 figures, 10 tables |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.05802 [cs.CL] |
| (or arXiv:2608.05802v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.05802
arXiv-issued DOI via DataCite (pending registration)
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