Interplay-LM-Reasoning/context

---
license: mit
task_categories:
- question-answering
---

<h1 align="center">
On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
</h1>

<div align="center">

<a href="https://chenlong-clock.github.io">Charlie Zhang</a>, <a href="https://www.phontron.com">Graham Neubig</a>,
<a href="https://xiangyue9607.github.io">Xiang Yue</a>

Carnegie Mellon University, Language Technologies Institute

</div>

<div align="center">

[](https://arxiv.org/abs/2512.07783)
[](LICENSE)

</div>

## Does Reinforcement Learning Truly Extend Reasoning?

This work explores the discrepancy in views on RL's effectiveness in extending language models' reasoning abilities. Some characterize RL as a capability refiner, while others see it as inducing new compositional skills. This challenge stems from a lack of control in modern training pipelines. Our work aims to resolve this conflict through controlled analysis, going beyond the initial description that this repository contains mid-training related checkpoints in the extrapolation tasks.

## 🔍 Overview

Our paper builds a fully controlled experimental framework to analyze how pre-training, mid-training, and RL-based post-training jointly shape the reasoning abilities of language models. Using synthetic math-style reasoning tasks with explicit atomic operations and process-verifiable reasoning traces, we study:

* **Extrapolative generalization** to more complex compositions (deeper dependency graphs).
* **Contextual generalization** across diverse surface forms and linguistic contexts.
* How **RL interacts** with prior knowledge, and when it yields **genuine capability gains** beyond pre-training.

## Code

The code for this work is released at the following GitHub repository: [https://github.com/Interplay-LM-Reasoning/Interplay-LM-Reasoning](https://github.com/Interplay-LM-Reasoning/Interplay-LM-Reasoning)

## 📚 Citation

If you find this work or code useful, please consider citing:

```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},
}
```