On-Policy Delta Distillation for Multilingual Math Reasoning

Byeongho Heo, Jaehui Hwang, Sangdoo Yun, Dongyoon Han

On-Policy Delta Distillation for Multilingual Math Reasoning: 32 upvotes on Hugging Face Daily Papers, #12 of 30 papers on 2026-08-07. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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