metadata
license: apache-2.0
datasets:
- openbmb/UltraFeedback
language:
- en
This is a model released for our paper: REBEL: Reinforcement Learning via Regressing Relative Rewards.
REBEL-Llama-3-epoch_2
This model is developed with REBEL based on Meta-Llama-3-8B-Instruct with FsfairX-LLaMA3-RM-v0.1 as the reward model and UltraFeedback dataset. The training code is available at https://github.com/ZhaolinGao/REBEL. We collect online generations during each iteration with a batch size of 32.
Links to Other Model
Evaluations
| Model | AlpacaEval 2.0 LC Win Rate |
AlpacaEval 2.0 Win Rate |
MT-Bench Average |
MMLU (5-shot) |
GSM8K (5-shot) |
|---|---|---|---|---|---|
| REBEL-OpenChat-3.5 | 17.3 | 12.8 | 8.06 | 63.7 | 68.8 |
| REBEL-Llama-3 | 30.1 | 32.6 | 8.16 | 65.8 | 75.6 |
| REBEL-Llama-3-epoch_2 | 31.3 | 34.2 | 7.83 | 65.4 | 75.4 |
| REBEL-Llama-3-Armo-iter_1 | 48.3 | 41.8 | 8.13 | 66.3 | 75.8 |
| REBEL-Llama-3-Armo-iter_2 | 50.0 | 48.5 | 8.07 | 65.9 | 75.4 |
| REBEL-Llama-3-Armo-iter_3 | 49.7 | 48.1 | 8.01 | 66.0 | 75.7 |
Citation
Please cite our paper if you use this model in your own work:
@misc{gao2024rebel,
title={REBEL: Reinforcement Learning via Regressing Relative Rewards},
author={Zhaolin Gao and Jonathan D. Chang and Wenhao Zhan and Owen Oertell and Gokul Swamy and Kianté Brantley and Thorsten Joachims and J. Andrew Bagnell and Jason D. Lee and Wen Sun},
year={2024},
eprint={2404.16767},
archivePrefix={arXiv},
primaryClass={cs.LG}
}