AI 中文总结
针对低资源东南亚语言大模型推理的跨语言崩溃与微调瓶颈，提出OSCD算法，结合翻译智能体循环与联合嵌入语义对齐，在AIME25等基准上实现数学推理的显著提升。
AI 中文摘要
大语言模型在推理能力上取得了显著进展，但在低资源原生场景中，许多模型存在跨语言崩溃问题，在需要复杂逻辑推理的中间步骤会退化为使用英语，这给策略优化带来了冷启动瓶颈，而标准微调则存在因跨语言表示漂移导致的灾难性遗忘风险。为应对这些挑战，本文提出Onramp序列跨蒸馏（OSCD）算法，这是一种后训练算法，在生成式训练推进过程中，通过集成翻译智能体循环将高资源语言的推理轨迹投影到低资源语言的词汇子空间，确保动态生成的参考样本能稳定且高效地翻译，用于微调。该算法还结合了参考语言和目标语言推理轨迹的联合嵌入语义对齐，从而弥合成对跨语言表示差距。使用AIME25和HMMT25基准进行的综合评估表明，OSCD在原生东南亚语言的数学推理上实现了最高达3.2倍的整体提升，其中联合嵌入语义对齐组件在语言去偏方面，比仅翻译基线实现了最高达6.4%的提升。
英文摘要
Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual collapse, reverting to English during intermediate steps that require complex logical reasoning. This presents a cold-start bottleneck for policy optimization, whereas standard fine-tuning risks catastrophic forgetting due to cross-lingual representation drift. To address these challenges, we introduce the Onramp-Sequence Cross-Distillation (OSCD), a post-training algorithm that projects high-resource reasoning trajectories into low-resource vocabulary subspaces during generative training rollouts via an integrated translator agentic loop, ensuring the stable and efficient translation of dynamically generated reference samples for fine-tuning. This is coupled with joint-embedding semantic alignment of both reference and target-language reasoning traces, thereby bridging the pairwise cross-lingual representational gaps. Comprehensive evaluations using the AIME25 and HMMT25 benchmarks demonstrate that OSCD yields up to 3.2 times overall improvements in native Southeast Asian languages for mathematical reasoning, of which the joint-embedding semantic alignment component contributes up to 6.4% improvements in linguistic debiasing over translation-only baselines.
Comments22 pages, 16 figures, 12 tables
