Aligning Large Language Models by On-Policy Self-Judgment
<|reference_start|>Aligning Large Language Models by On-Policy Self-Judgment: Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we present a novel alignment framework, SELF-JUDGE that (1) does on-policy learning and 2) is parameter efficient, as it does not require an additional RM for evaluating the samples for on-policy learning. To this end, we propose Judge-augmented Supervised Fine-Tuning (JSFT) to train a single model to act as both a policy and a judge. Specifically, we view the pairwise judgment task, choosing the better response from a response pair, as a special case of the instruction-following task. The resulting model can judge preferences of on-the-fly responses from current policy initialized from itself. Experimental results show the efficacy of SELF-JUDGE, outperforming baselines in preference benchmarks. We also show that the rejecting sampling by itself can improve performance further without an additional evaluator.<|reference_end|>
@article{lee2024aligning,
    title={Aligning Large Language Models by On-Policy Self-Judgment},
    author={Sangkyu Lee, Sungdong Kim, Ashkan Yousefpour, Minjoon Seo, Kang Min
  Yoo, Youngjae Yu},
    journal={arXiv preprint arXiv:2402.11253},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11253},
    primaryClass={cs.LG cs.AI cs.CL}
}