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}
}