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Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)

Mistral7B-PairRM-SPPO

This model was developed using Self-Play Preference Optimization, based on the mistralai/Mistral-7B-Instruct-v0.2 architecture as starting point. We utilized the prompt sets from the openbmb/UltraFeedback dataset, splited to 3 parts for 3 iterations by snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset. All responses used are synthetic.

While K = 5, this model uses three samples to estimate the soft probabilities P(y_w > y_l) and P(y_l > y_w). These samples include the winner, the loser, and another random sample. This approach has shown to deliver better performance on AlpacaEval 2.0 compared to the results reported in our paper.

❗Please refer to the original checkpoint at UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3 as reported in our paper. We anticipate that the version in the paper demonstrates a more consistent performance improvement across all evaluation tasks.

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Model Description

  • Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
  • Language(s) (NLP): Primarily English
  • License: Apache-2.0
  • Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2

AlpacaEval Leaderboard Evaluation Results

Model LC. Win Rate Win Rate Avg. Length
Mistral7B-PairRM-SPPO 30.46 32.14 2114

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • eta: 1000
  • per_device_train_batch_size: 8
  • gradient_accumulation_steps: 1
  • seed: 42
  • distributed_type: deepspeed_zero3
  • num_devices: 8
  • optimizer: RMSProp
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_train_epochs: 18.0 (stop at epoch=1.0)

Citation

@misc{wu2024self,
      title={Self-Play Preference Optimization for Language Model Alignment}, 
      author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
      year={2024},
      eprint={2405.00675},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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