Training Foundation Models with a Single Algorithm
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper argues that the usual staged pipeline for foundation models (pretraining, midtraining, SFT, and RL) is not fundamentally tied to separate algorithms. By deriving RL objectives as maximum-likelihood fitting to model-generated target distributions, the authors place all stages under one likelihood-based algorithm, called unified training. They implement a system that mixes static data and RL generations in a single trainer. The authors report that, across controlled and real-world experiments, unified training improves the accuracy-compute tradeoff and task coverage, preserves broader capabilities, and gives a stronger starting point for continual learning.
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- Primary sourceHugging Face Daily Papers
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The paper argues that the usual staged pipeline for foundation models (pretraining, midtraining, SFT, and RL) is not fundamentally tied to separate algorithms. By deriving RL objectives as maximum-likelihood fitting to model-generated target distributions, the authors place all stages under one likelihood-based algorithm, called unified training. They implement a system that mixes static data and RL generations in a single trainer. The authors report that, across controlled and real-world experiments, unified training improves the accuracy-compute tradeoff and task coverage, preserves broader capabilities, and gives a stronger starting point for continual learning.
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Key findings from the stored brief
- RL objectives can be derived as maximum-likelihood fitting to model-generated target distributions.
- All training stages can be placed under a single likelihood-based algorithm called unified training.
- A single trainer mixing static data and RL generations improves the accuracy-compute tradeoff and task coverage.
- Unified training preserves broader capabilities and provides a stronger starting point for continual learning.
Source claims not independently checked; the supplied text is the abstract and does not detail experimental setups or quantitative results.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Why this may matter
If staged training is a special case of one algorithm, the design question becomes choosing data, proportions, and timing. This could simplify training pipelines and make compute use more efficient, though the claims need verification against the full experiments.
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