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Interplay-LM-Reasoning/
extrapolation_rl

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Interplay-LM Extrapolation RL Models

This repository is organized by experiment setting. Each top-level directory corresponds to one pretraining mixture used in the extrapolation experiments.

Within each setting:

base/ stores the base model used to initialize RL.
rl/ stores the final RL checkpoints for each experiment variant.

Only inference-relevant Hugging Face files are included.

Included settings

id2-10_0.2easy_0.3medium_0.5hard
id2-10_0.5easy_0.3medium_0.2hard
id2-10_0.4995easy_0.4995medium_0.001hard
id2-10_0.475easy_0.475medium_0.05hard

Load

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Interplay-LM-Reasoning/extrapolation_rl"
subdir = "id2-10_0.5easy_0.3medium_0.2hard/rl/op11-14_uniform"

tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subdir)
model = AutoModelForCausalLM.from_pretrained(repo_id, subfolder=subdir)

Citation

bibtex

@misc{zhang2025interplaypretrainingmidtrainingrl,
      title={On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models},
      author={Charlie Zhang and Graham Neubig and Xiang Yue},
      year={2025},
      eprint={2512.07783},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.07783},
}