R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing.
Traditional approaches typically frame RE as a supervised learning problem,
directly mapping context to labels-an approach that often suffers from poor
out-of-domain (OOD) generalization. Inspired by the workflow of human
annotators, we reframe RE as a reasoning task guided by annotation guidelines
and introduce R1-RE, the first reinforcement learning with verifiable reward
(RLVR) framework for RE tasks. Our method elicits the reasoning abilities of
small language models for annotation tasks, resulting in significantly improved
OOD robustness. We evaluate our approach on the public Sem-2010 dataset and a
private MDKG dataset. The R1-RE-7B model attains an average OOD accuracy of
approximately 70%, on par with leading proprietary models such as GPT-4o.
Additionally, our comprehensive analysis provides novel insights into the
training dynamics and emergent reasoning behaviors of the RLVR paradigm for RE.
686d64b6cb5725779c60b239
Runpeng Dai
claimed_verified
2025-07-09T08:51:15.444Z
65037565da2d88e201f63b7a
/avatars/d1b6ce17236360e9583b8bb4cb87e506.svg
Runpeng Dai
user
Leo-Dai
686d64b6cb5725779c60b23a
Tong Zheng
claimed_verified
2025-07-09T08:51:12.601Z
6623ea65b642e29cdf90a1b4
/avatars/e32e90574c1162b2be87ed78604e3e4d.svg
TongZheng
user
TongZheng1999
686d64b6cb5725779c60b23b
Run Yang
686d64b6cb5725779c60b23c
Hongtu Zhu
R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing.
Traditional approaches typically frame RE as a supervised learning problem,
directly mapping context to labels-an approach that often suffers from poor
out-of-domain (OOD) generalization. Inspired by the workflow of human
annotators, we reframe RE as a reasoning task guided by annotation guidelines
and introduce R1-RE, the first reinforcement learning with verifiable reward
(RLVR) framework for RE tasks. Our method elicits the reasoning abilities of
small language models for annotation tasks, resulting in significantly improved
OOD robustness. We evaluate our approach on the public Sem-2010 dataset and a
private MDKG dataset. The R1-RE-7B model attains an average OOD accuracy of
approximately 70%, on par with leading proprietary models such as GPT-4o.
Additionally, our comprehensive analysis provides novel insights into the
training dynamics and emergent reasoning behaviors of the RLVR paradigm for RE.
R1-RE, a reinforcement learning with verifiable reward framework, enhances out-of-domain robustness in relationship extraction by leveraging small language models and annotation guidelines.
reinforcement learning verifiable reward RLVR relationship extraction out-of-domain OOD Sem-2010 dataset MDKG dataset GPT-4o training dynamics emergent reasoning behaviors