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Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning

Paper · 2026 · Chongyuan Dai, Jinpeng Hu, Hongchang Shi et al.

Summary

Psyche-R1 is a Chinese psychological LLM that combines three abilities usually built separately: empathetic counselling, psychology domain knowledge, and explicit reasoning. Earlier psychology-oriented models either focused on emotional support or added domain knowledge with little reasoning. General reasoning models, in turn, perform poorly on psychology tasks.

The authors built a training corpus from psychology textbooks, public Chinese question banks, LLM-synthesised material for underrepresented subfields, and counselling exchanges from mental-health communities. They cleaned and deduplicated it, then generated multi-step rationales for each question and refined them by alternating revisions of the prompt and the rationale.

Several LLMs answered every question to sort the data. Questions that every model got wrong went to GRPO reinforcement learning. All remaining items, including the empathetic dialogues, were used for supervised fine-tuning.

The resulting 7B model clearly beats models of similar size on psychology benchmarks and performs roughly on par with the 671B DeepSeek-R1. This suggests that carefully curated domain data can partly substitute for model scale in sensitive application areas.

Key points

  • Released data: over 75k psychology questions paired with rationales, plus 73k empathetic dialogues. The dialogues come from Yixinli, Jiandanxinli and Zhihu. Code and data are on GitHub (MindIntLab-HFUT/Psyche-R1).
  • QA filtering: a raw pool of about 210k QA pairs was reduced to roughly 90k. The steps were MinHash LSH deduplication, few-shot LLM quality filtering, and semi-automatic review by 10 students.
  • Tools used in the pipeline:
    • OLMOCR for converting documents to text.
    • Qwen2.5-72B-Instruct for distilling supplementary questions.
    • GPT-4o as the judge for empathy evaluation, run on 200 sampled test instances.
  • Hybrid training rule: if at least one model in the multi-LLM selector answers correctly, the item goes to SFT. If all models fail, it goes to GRPO.
  • Evaluation and limits: the main benchmark is PCEB, which covers theory, case and moral questions in single- and multiple-choice formats. The largest gains appear on its challenging subset. The authors list two main limitations: the model is tied to Chinese language and culture, and it is restricted to a 7B backbone.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Science and Technology of ChinaHefei · China108 works · 112,698 citations🎓Chinese University of Hong Kong, ShenzhenShenzhen · China47 works · 2,596 citations🎓Hefei University of TechnologyHefei · China10 works · 1,615 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksMedicine · 241 worksHealth Informatics · 184 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Dan Guo, Xun Yang and Meng Wang. 2026. Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1141

@inproceedings{dai2026psyche,
  title = {{Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning}},
  author = {Chongyuan Dai, Jinpeng Hu, Hongchang Shi, Zhuo Li, Dan Guo, Xun Yang and Meng Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1141},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1141}
}

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