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| Method | Dataset | Winrate at Turn | |||||
|---|---|---|---|---|---|---|---|
| h = 1 | h = 2 | h = 3 | h = 4 | H = 5 | avg | ||
| Llama-3.1-70B-it | N/A | 70.4 | 66.4 | 61.0 | 53.0 | 55.4 | 61.24 |
| REFUEL-Llama-3-Armo-iter_1 | REFUEL-Ultrainteract-Llama-3-Armo-iter_1 | 54.6 | 53.6 | 57.8 | 56.2 | 59.4 | 56.32 |
| REFUEL-Llama-3-Armo-iter_2 | REFUEL-Ultrainteract-Llama-3-Armo-iter_2 | 55.2 | 53.4 | 58.8 | 57.2 | 58.6 | 56.64 |
@misc{gao2024regressingrelativefutureefficient,
title={Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF},
author={Zhaolin Gao and Wenhao Zhan and Jonathan D. Chang and Gokul Swamy and Kianté Brantley and Jason D. Lee and Wen Sun},
year={2024},
eprint={2410.04612},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.04612},
}