这个TwT框架让翻译模型根据难度动态调整思考量，7B和14B的小模型打过大模型，token还省了30%到60%，做多领域翻译的可以看看。
TwT（Translation with Thought）是一个难度自适应的多领域机器翻译框架，通过强化学习在直觉与深思推理间动态切换。该模型先利用DeepSeek-R1蒸馏并由GPT-4o重写的长思维链进行监督微调，再通过混合奖励优化翻译质量与推理效率。在15个基准、3种已见语言和59种未见语言上，TwT-7B和TwT-14B的翻译质量超过更大的SOTA推理模型，同时token用量减少32%至60%。
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt reasoning effort based on difficulty, we propose TwT (Translation with Thought), a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning. TwT is trained in two stages: (1) supervised fine-tuning on difficulty-aware long chain-of-thought traces distilled from DeepSeek-R1 and rewritten by GPT-4o to reflect human-like reasoning economy, and (2) reinforcement learning with a hybrid reward to optimize translation quality and reasoning efficiency. Evaluated on 15 benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen languages, with ablations across three backbone models, TwT-7B and TwT-14B outperform much larger SOTA reasoning models in translation quality, while reducing token usage by 32--60\%. These results confirm that aligning translation behavior with cognitive principles enables robust generalization, high translation quality, and efficient reasoning in MDMT.