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Biao Fu

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Preprint Aug 2026

PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation

PAMT is proposed, a process-aligned training framework that combines cold-start domain-aware Long-CoT supervision with reinforcement learning that improves over base models, outperforms MT-specialized baselines on average, and remains competitive with strong LLMs/LRMs across in-domain, OOD, and multilingual settings.

Yongshi Ye, Biao Fu, Chongxuan Huang et al. · 0 citations
Conference Open access Jul 2026

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

TwT (Translation with Thought), a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning, confirms that aligning translation behavior with cognitive principles enables robust generalization, high translation quality, and efficient reasoning in MDMT.

Yongshi Ye, Biao Fu, Chongxuan Huang et al. · 0 citations