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Official repo for Learning to Reason for Long-Form Story Generation

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# Learning to Reason for Long-Form Story Generation

Official repo for Learning to Reason for Long-Form Story Generation, available on [arxiv](https://arxiv.org/abs/2503.22828).

This work presents a new RL-reward paradigm, Verifiable Rewards via Completion Likelihood Improvement (VR-CLI), which is then used to train a model to predict useful plans for the next chapter of a story.

This repo contains five parts:

1. `setup_data`: Compile a Next-Chapter Prediction dataset, used for training and story-generation
2. `rl_training`: Train a model using our VR-CLI reward paradigm, using either the NCP task or another task of your choosing
3. `sft_training`: Train a model using supervised finetuning on the NCP task
4. `story_generation`: Generate reasoning and story continuations using either a pretrained model, or a model you have trained yourself
5. `evaluation`: Replicate our evaluations of the story generation models using human annotations and automated metrics

Consult the `instructions.md` files in each directory for more details.

## Citation

If you find this work useful, please cite it as follows:

```bibtex
@misc{gurung2025learningreasonlongformstory,
      title={Learning to Reason for Long-Form Story Generation}, 
      author={Alexander Gurung and Mirella Lapata},
      year={2025},
      eprint={2503.22828},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.22828}, 
}
```

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