Researchers Test Unified Training Rule in Two Foundation Model Studies

Researchers affiliated with Carnegie Mellon University, Johns Hopkins University and Aurora Innovation tested a unified training framework in two foundation-model experiments, finding results that varied across tasks, computing budgets and data schedules. The framework is designed to apply one likelihood-based objective to fixed examples and model-generated responses selected with reward signals. It adjusts the data mix over time and is intended to span pretraining, midtraining, supervised fine-tuning and suitable reinforcement-learning objectives. The reported work covers two experimental settings; it does not demonstrate results for every stage in that sequence.

Source: TokenPost