Image credit: VentureBeat with Ideogram Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Language models can generalize better when left to create their own solutions, a new study by Hong Kong University and University of California, Berkeley, shows. The findings, which apply to both large language models (LLMs) and vision language models (VLMs), challenge one of the main beliefs of the LLM community — that models require hand-labeled training examples. In fact, the researchers show that training models on too many hand-crafted examples can have adverse effects on the model’s ability to generalize to unseen data. SFT vs RL in model training For a long time, supervised fine-tuning (SFT) has been the gold standard for training LLMs and […]
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