frontier models
Pretraining and Reinforcement Learning Enter One Trainer
A likelihood-based formulation mixed static data and model-generated reinforcement-learning targets inside one training system.
Summary
A likelihood-based formulation mixed static data and model-generated reinforcement-learning targets inside one training system.
Foundation-model development is usually divided into pretraining, midtraining, supervised fine-tuning and reinforcement learning, each with different algorithms. This work derives reinforcement-learning objectives as maximum-likelihood fits to model-generated target distributions, then mixes static examples and reinforcement-learning generations in one trainer. Controlled and real-world experiments reportedly improved the accuracy-compute tradeoff and task coverage while preserving broader capabilities. The paper presents staged training as one special schedule inside a unified framework; it does not show that data selection, reward design or safety objectives become interchangeable.
Why it matters
A likelihood-based formulation mixed static data and model-generated reinforcement-learning targets inside one training system.
Limits and context
- The paper presents staged training as one special schedule inside a unified framework; it does not show that data selection, reward design or safety objectives become interchangeable.
Key claims
A likelihood-based formulation mixed static data and model-generated reinforcement-learning targets inside one training system.
Qualification: The paper presents staged training as one special schedule inside a unified framework; it does not show that data selection, reward design or safety objectives become interchangeable.
Evidence: source-2026-11-01-010
Sources
- arXiv preprint 2610.40564, original v1arXiv · primary research
Corrections
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