README.md

metadata

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

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 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), and CPsyExam test set proposed by Zhao et al. (2024). It also demonstates better performance in empathy on SoulChat2.0 test set (Xie et al. 2025).

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 on GitHub.

Citation

If this work is helpful, please kindly cite as:

@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}
}