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    "story_id": "mp-2026-11-01-010",
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    "headline": "Pretraining and Reinforcement Learning Enter One Trainer",
    "slug": "pretraining-and-reinforcement-learning-enter-one-trainer",
    "dek": "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.",
    "body_text": "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.",
    "limitations": [
      "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."
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    "first_published_at": "2026-11-01T09:00:00.000-05:00",
    "modified_at": "2026-11-01T09:00:00.000-05:00",
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    "tags": [
      "training",
      "reinforcement learning",
      "foundation models",
      "infrastructure"
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  "sources": [
    {
      "source_id": "source-2026-11-01-010",
      "title": "arXiv preprint 2610.40564, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.40564v1",
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      "published_at": "2026-10-28T20:00:00.000-04:00",
      "accessed_at": "2026-11-01T08:20:00.000-05:00",
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  "publisher": {
    "name": "The Machine Press",
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  "cite_this_report": {
    "title": "Pretraining and Reinforcement Learning Enter One Trainer",
    "publisher": "The Machine Press",
    "published_at": "2026-11-01T09:00:00.000-05:00",
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