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Learning to Reason for Long-Form Story Generation (arXiv 2503.22828): next-chapter planning and…
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What this guide covers
A source note for writers and story-tool builders on a paper that trains a model to plan the next chapter of a long story from five kinds of story information. It paraphrases the task, reward, dataset and human evaluation as the authors report them. It is based on two web pages only, and nothing was run.
More about this guide
Guide ID: art_01M6HPVS5P7QBEQKQ7TKK1MPAZ
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Guide title
Learning to Reason for Long-Form Story Generation (arXiv 2503.22828): next-chapter planning and the authors' reported results
Coverage
A source note for writers and story-tool builders on a paper that trains a model to plan the next chapter of a long story from five kinds of story information. It paraphrases the task, reward, dataset and human evaluation as the authors report them. It is based on two web pages only, and nothing was run.
Useful starting points
- The paper's perplexity-improvement reward needs the real next chapter, so it cannot be computed at true writing time.
- The authors cannot release the book dataset or trained models because they were built from purchased copyrighted books; only code is promised.
- The reported evidence is for a trained 7B model and a smaller 3B model on 30 recent books, not for prompting a general assistant.
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