文言文当推理语言：Wenyan Thinking 让中文模型思考更省 token
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs
这批研究者把 Qwen3 的推理过程微调成文言文，更省 token、漏答更少，还压过了同样压缩过的白话版。
论文提出 Wenyan Thinking，让模型的可见推理过程用文言文书写，最终答案仍用简短白话直接给出。研究者把现代白话推理轨迹改写成文言，据此微调 Qwen3-4B-Thinking-2507。为区分换语体和单纯压缩，他们用同一批数据、同一教师模型和 SFT 流程构造了紧凑白话对照组。在 MMLU-ProX-Lite-ZH、CEVAL 和 AGIEval-CN 的受限解码评测中，文言推理明显缩短推理长度并降低漏答率。在主基准上，文言版仍优于同等压缩程度的白话对照。
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs
Visible reasoning traces of Chinese reasoning-oriented LLMs are typically written in modern vernacular Chinese, which may be verbose under limited decoding budgets. We ask whether the linguistic register of reasoning can be used to improve efficiency. We propose Wenyan Thinking, which emits intermediate reasoning in Classical Chinese (wenyanwen) while retaining a short direct-answer output. Starting from modern-Chinese reasoning traces, we rewrite only the reasoning field into wenyan and fine-tune Qwen3-4B-Thinking-2507 on the rewritten supervision. To distinguish register shift from generic compression, we construct a compact modern-Chinese control using the same source data, teacher family, base model, and SFT recipe. Across budget-sensitive evaluations on MMLU-ProX-Lite-ZH, CEVAL, and AGIEval-CN, Wenyan Thinking substantially reduces reasoning length and missing-answer rates under constrained decoding. Compact rewriting explains much of the efficiency gain, while wenyan consistently outperforms the compact modern-Chinese control on the primary benchmark and remains competitive across the additional benchmarks. These results identify linguistic register as a practical design axis for efficient visible reasoning in Chinese LLMs.
