Kavli Affiliate: Zhuo Li
| First 5 Authors: Chongyuan Dai, Chongyuan Dai, , ,
| Summary:
Amidst a shortage of qualified mental health professionals, the integration
of large language models (LLMs) into psychological applications offers a
promising way to alleviate the growing burden of mental health disorders.
Recent reasoning-augmented LLMs have achieved remarkable performance in
mathematics and programming, while research in the psychological domain has
predominantly emphasized emotional support and empathetic dialogue, with
limited attention to reasoning mechanisms that are beneficial to generating
reliable responses. Therefore, in this paper, we propose Psyche-R1, the first
Chinese psychological LLM that jointly integrates empathy, psychological
expertise, and reasoning, built upon a novel data curation pipeline.
Specifically, we design a comprehensive data synthesis pipeline that produces
over 75k high-quality psychological questions paired with detailed rationales,
generated through chain-of-thought (CoT) reasoning and iterative
prompt-rationale optimization, along with 73k empathetic dialogues.
Subsequently, we employ a hybrid training strategy wherein challenging samples
are identified through a multi-LLM cross-selection strategy for group relative
policy optimization (GRPO) to improve reasoning ability, while the remaining
data is used for supervised fine-tuning (SFT) to enhance empathetic response
generation and psychological domain knowledge. Extensive experiment results
demonstrate the effectiveness of the Psyche-R1 across several psychological
benchmarks, where our 7B Psyche-R1 achieves comparable results to 671B
DeepSeek-R1.
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