Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)
Llama-3-Instruct-8B-SPPO-Iter2
This model was developed using Self-Play Preference Optimization at iteration 2, based on the meta-llama/Meta-Llama-3-8B-Instruct 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.
Links to Other Models
Model Description
- Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
- Language(s) (NLP): Primarily English
- License: Apache-2.0
- Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
AlpacaEval Leaderboard Evaluation Results
| Model | LC. Win Rate | Win Rate | Avg. Length |
|---|---|---|---|
| Llama-3-8B-SPPO Iter1 | 31.73 | 31.74 | 1962 |
| Llama-3-8B-SPPO Iter2 | 35.15 | 35.98 | 2021 |
| Llama-3-8B-SPPO Iter3 | 38.77 | 39.85 | 2066 |
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 | |
|---|---|---|---|---|---|---|---|
| Llama-3-8B-SPPO Iter1 | 63.82 | 54.96 | 76.40 | 75.44 | 79.80 | 65.65 | 69.35 |
| Llama-3-8B-SPPO Iter2 | 64.93 | 56.48 | 76.87 | 75.13 | 80.39 | 65.67 | 69.91 |
| Llama-3-8B-SPPO Iter3 | 65.19 | 58.04 | 77.11 | 74.91 | 80.86 | 65.60 | 70.29 |
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: 6.0 (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}
}