Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms. We show t
Foundation-model training stages can be unified under a single likelihood-based algorithm that blends static data and reinforcement learning generations, improving efficiency and capability retention.
Foundation models are typically trained in stages: pretraining, midtraining, supervised fine-tuning (SFT), and reinforcement learning (RL), with changes in both data and learning algorithms. We show that this algorithmic separation is not fundamental. By deriving suitable RL objectives as maximum-likelihood fitting to model-generated target distributions, we place all of these stages under a single likelihood-based training algorithm, unified training. This perspective turns the foundation-model training lifecycle into a problem of choosing which data to train on, in what proportions, and when, with conventional staged training as a special case. We instantiate this framework in a unified…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Training Foundation Models with a Single Algorithm was first seen on Hugging Face · Daily papers on Oct 31, 2026. Since then it has appeared in 29 checks on 4 different days. Its best position was #21 of 30, reached on Nov 2, 09:28 UTC. It dropped off the list after Nov 3, 01:28 UTC.
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It was first seen on Oct 31, 2026 and has been on the list on 4 days (29 checks) since.