Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)
Mistral7B-PairRM-SPPO-Iter3
This model was developed using Self-Play Preference Optimization at iteration 3, 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.
This is the model reported in the paper , with K=5 (generate 5 responses per iteration). We attached the Arena-Hard eval results in this model page.
Links to Other Models
- Mistral7B-PairRM-SPPO-Iter1
- Mistral7B-PairRM-SPPO-Iter2
- Mistral7B-PairRM-SPPO-Iter3
- Mistral7B-PairRM-SPPO
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 Iter 1 | 24.79 | 23.51 | 1855 |
Mistral7B-PairRM-SPPO Iter 2 | 26.89 | 27.62 | 2019 |
Mistral7B-PairRM-SPPO Iter 3 | 28.53 | 31.02 | 2163 |
Mistral7B-PairRM-SPPO Iter 1 (best-of-16) | 28.71 | 27.77 | 1901 |
Mistral7B-PairRM-SPPO Iter 2 (best-of-16) | 31.23 | 32.12 | 2035 |
Mistral7B-PairRM-SPPO Iter 3 (best-of-16) | 32.13 | 34.94 | 2174 |
Arena-Hard Evaluation Results
Model | Score | 95% CI | average # Tokens |
---|---|---|---|
Mistral7B-PairRM-SPPO-Iter3 | 23.3 | (-1.8, 1.8) | 578 |
Open LLM Leaderboard Evaluation Results
Results are reported by using lm-evaluation-harness v0.4.1
arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu | average | |
---|---|---|---|---|---|---|---|
Mistral7B-PairRM-SPPO Iter 1 | 65.02 | 69.4 | 77.82 | 43.82 | 85.11 | 58.84 | 66.67 |
Mistral7B-PairRM-SPPO Iter 2 | 65.53 | 69.55 | 77.03 | 44.35 | 85.29 | 58.72 | 66.75 |
Mistral7B-PairRM-SPPO Iter 3 | 65.36 | 69.97 | 76.8 | 42.68 | 85.16 | 58.45 | 66.4 |
MT-Bench Evaluation Results
1st Turn | 2nd Turn | Average | |
---|---|---|---|
Mistral7B-PairRM-SPPO Iter 1 | 7.63 | 6.79 | 7.21 |
Mistral7B-PairRM-SPPO Iter 2 | 7.90 | 7.08 | 7.49 |
Mistral7B-PairRM-SPPO Iter 3 | 7.84 | 7.34 | 7.59 |
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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