Paper places pretraining, fine-tuning and RL under one likelihood-based training algorithm

Authors from Carnegie Mellon, Johns Hopkins and others derive RL objectives as maximum-likelihood fitting to model-generated targets and report a better accuracy-compute trade-off; it is a preprint without peer review.