This is a model released for our paper: REBEL: Reinforcement Learning via Regressing Relative Rewards.
REBEL-OpenChat-3.5
This model is developed with REBEL based on OpenChat-3.5 with Starling-RM-7B-alpha as the reward model and Nectar 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 2.0LC Win Rate | AlpacaEval 2.0Win Rate | MT-BenchAverage | 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}
}