Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering

• URL: http://arxiv.org/abs/2503.11314v1
• Date: Fri, 14 Mar 2025 11:30:37 GMT
• Title: Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
• Authors: Xinyu Tang, Xiaolei Wang, Zhihao Lv, Yingqian Min, Wayne Xin Zhao, Binbin Hu, Ziqi Liu, Zhiqiang Zhang,
• Abstract summary: Existing work finds that the capability of long CoT reasoning can be efficiently elicited by tuning on only a few examples.<n>This motivates us to investigate whether long CoT reasoning is a general capability for LLMs.<n>We propose GLoRE, a novel representation engineering method to unleash the general long CoT reasoning capabilities of LLMs.
• Score: 59.34894142132706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capability of long CoT reasoning can be efficiently elicited by tuning on only a few examples and can easily transfer to other tasks. This motivates us to investigate whether long CoT reasoning is a general capability for LLMs. In this work, we conduct an empirical analysis for this question from the perspective of representation. We find that LLMs do encode long CoT reasoning as a general capability, with a clear distinction from vanilla CoTs. Furthermore, domain-specific representations are also required for the effective transfer of long CoT reasoning. Inspired by these findings, we propose GLoRE, a novel representation engineering method to unleash the general long CoT reasoning capabilities of LLMs. Extensive experiments demonstrate the effectiveness and efficiency of GLoRE in both in-domain and cross-domain scenarios.

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