| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| tags: |
| - llama-factory |
| - verl |
| - grpo-training |
| model-index: |
| - name: Psyche-R1 |
| results: [] |
| language: |
| - zh |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| <div style="display: flex; align-items: center;"> |
| <img src="logo.png" alt="Psyche-R1 logo" style="height: 2em; margin-right: 10px;"> |
| <h1 style="margin: 0;">Psyche-R1</h1> |
| </div> |
|
|
| **\[ACL 2026\]** Repositories for our paper: *Psyche-r1: Towards reliable psychological llms through unified empathy, expertise, and reasoning* |
|
|
| We propose the first Chinese psychological reasoning LLM that unifies empathy, expertise, and reasoning. |
|
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| This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on our proposed dataset encompassing psychological questions paired with detailed rationales, and empathetic single-turn dialogues. |
|
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| We conduct a hybrid training strategy, including SFT and GRPO training. We will present detailed training hyperparameters later. |
|
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| It achieves comparable performance to DeepSeek-R1 on several psychology benchmarks, including psychology counselor examination benchmark (PCEB) proposed by [Hu et al. (2024)](https://ieeexplore.ieee.org/abstract/document/10772313), and CPsyExam test set proposed by [Zhao et al. (2024)](https://aclanthology.org/anthology-files/anthology-files/pdf/coling/2025.coling-main.745.pdf). It also demonstates better performance in empathy on [SoulChat2.0 test set (Xie et al. 2025)](https://aclanthology.org/2025.acl-long.55.pdf). |
|
|
| ## Training procedure |
|
|
| ### SFT Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 8 |
| - gradient_accumulation_steps: 16 |
| - total_train_batch_size: 256 |
| - total_eval_batch_size: 8 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 2.0 |
| |
| ### GRPO Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 1e-06 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 8 |
| - total_train_batch_size: 128 |
| - total_eval_batch_size: 8 |
| - ppo_mini_batch_size: 32 |
| - ppo_micro_batch_size_per_gpu: 20 |
| - kl_loss_coef: 0.001 |
| - lr_scheduler_warmup_steps: 10 |
| - num_epochs: 2.0 |
|
|
| ## Usage |
| For quick start, please see [MindIntLab-HFUT/Psyche-R1](https://github.com/MindIntLab-HFUT/Psyche-R1) on GitHub. |
|
|
| ## Citation |
| If this work is helpful, please kindly cite as: |
| ```bibtex |
| @misc{dai2025psycher1reliablepsychologicalllms, |
| title={Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning}, |
| author={Chongyuan Dai and Jinpeng Hu and Hongchang Shi and Zhuo Li and Xun Yang and Meng Wang}, |
| year={2025}, |
| eprint={2508.10848}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2508.10848}, |
| } |
| ``` |
