  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.
