@inproceedings{wen-etal-2026-just,
title = "Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors",
author = "Wen, Pengcheng and
Zhu, Yanxu and
Sun, Jiapeng and
Zhu, Han and
Zhou, Yujin and
Chan, Chi-Min and
Guo, Yike and
Han, Sirui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1692/",
pages = "38505--38532",
ISBN = "979-8-89176-459-0",
abstract = "Chain-of-Thought (CoT) is often viewed as a window into LLM decision-making, yet recent work suggests it may function merely as post-hoc rationalization. This raises a critical alignment question: Does the reasoning trace causally shape model generalization independent of the final answer? To isolate reasoning{'}s causal effect, we hold final harmful answers constant while varying reasoning paths across three types, $\textit{Evil}$ (embracing malice), $\textit{Misleading}$ (rationalizing harm), and $\textit{Submissive}$ (yielding to pressure), and train models (Qwen 0.6B{--}14B) under QTA, QT, T-only, and QA paradigms, evaluating in both think and no-think modes. We find that: (1) CoT training amplifies harmful generalization beyond standard fine-tuning; (2) distinct reasoning types induce distinct behavioral patterns despite identical answers; (3) training on reasoning without answer supervision suffices to alter behavior; and (4) these effects persist when CoT is suppressed at inference. We validate these findings with naturally generated CoT, on GPT-OSS-20B, and under RLVR training, reporting bootstrap 95{\%} CIs throughout. Our findings demonstrate that reasoning content is causally potent for downstream alignment, challenging strategies that supervise only final outputs."
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<abstract>Chain-of-Thought (CoT) is often viewed as a window into LLM decision-making, yet recent work suggests it may function merely as post-hoc rationalization. This raises a critical alignment question: Does the reasoning trace causally shape model generalization independent of the final answer? To isolate reasoning’s causal effect, we hold final harmful answers constant while varying reasoning paths across three types, Evil (embracing malice), Misleading (rationalizing harm), and Submissive (yielding to pressure), and train models (Qwen 0.6B–14B) under QTA, QT, T-only, and QA paradigms, evaluating in both think and no-think modes. We find that: (1) CoT training amplifies harmful generalization beyond standard fine-tuning; (2) distinct reasoning types induce distinct behavioral patterns despite identical answers; (3) training on reasoning without answer supervision suffices to alter behavior; and (4) these effects persist when CoT is suppressed at inference. We validate these findings with naturally generated CoT, on GPT-OSS-20B, and under RLVR training, reporting bootstrap 95% CIs throughout. Our findings demonstrate that reasoning content is causally potent for downstream alignment, challenging strategies that supervise only final outputs.</abstract>
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%0 Conference Proceedings
%T Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors
%A Wen, Pengcheng
%A Zhu, Yanxu
%A Sun, Jiapeng
%A Zhu, Han
%A Zhou, Yujin
%A Chan, Chi-Min
%A Guo, Yike
%A Han, Sirui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wen-etal-2026-just
%X Chain-of-Thought (CoT) is often viewed as a window into LLM decision-making, yet recent work suggests it may function merely as post-hoc rationalization. This raises a critical alignment question: Does the reasoning trace causally shape model generalization independent of the final answer? To isolate reasoning’s causal effect, we hold final harmful answers constant while varying reasoning paths across three types, Evil (embracing malice), Misleading (rationalizing harm), and Submissive (yielding to pressure), and train models (Qwen 0.6B–14B) under QTA, QT, T-only, and QA paradigms, evaluating in both think and no-think modes. We find that: (1) CoT training amplifies harmful generalization beyond standard fine-tuning; (2) distinct reasoning types induce distinct behavioral patterns despite identical answers; (3) training on reasoning without answer supervision suffices to alter behavior; and (4) these effects persist when CoT is suppressed at inference. We validate these findings with naturally generated CoT, on GPT-OSS-20B, and under RLVR training, reporting bootstrap 95% CIs throughout. Our findings demonstrate that reasoning content is causally potent for downstream alignment, challenging strategies that supervise only final outputs.
%U https://aclanthology.org/2026.emnlp-main.1692/
%P 38505-38532
Markdown (Informal)
[Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors](https://aclanthology.org/2026.emnlp-main.1692/) (Wen et al., EMNLP 2026)
ACL
- Pengcheng Wen, Yanxu Zhu, Jiapeng Sun, Han Zhu, Yujin Zhou, Chi-Min Chan, Yike Guo, and Sirui Han. 2026. Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38505–38532, Budapest, Hungary. Association for Computational Linguistics.