Psyche-R1

We propose the first Chinese psychological reasoning LLM that unifies empathy, expertise, and reasoning. 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. We conduct a hybrid training strategy, including SFT and GRPO training. We will present detailed training hyperparameters later. 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 @article{dai2025psyche, title={Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning}, author={Dai, Chongyuan and Hu, Jinpeng and Shi, Hongchang and Li, Zhuo and Yang, Xun and Wang, Meng}, journal={arXiv preprint arXiv:2508.10848}, year={2025} } ```