Train Long, Think Short: Curriculum Learning for Efficient Reasoning
Authors: name: Hasan Abed Al Kader Hammoud, name: Kumail Alhamoud, name: Abed Hammoud, name: Elie Bou-Zeid, name: Marzyeh Ghassemi, name: Bernard Ghanem

Recent work on enhancing the reasoning abilities of large language models
(LLMs) has introduced explicit length control as a means of constraining
computational cost while preserving accuracy. However, existing approaches rely
on fixed-length training budgets, which do not take advantage of the natural
progression from exploration to compression during learning. In this work, we
propose a curriculum learning strategy for length-controlled reasoning using
Group Relative Policy Optimization (GRPO). Our method starts with generous
token budgets and gradually tightens them over training, encouraging models to
first discover effective solution strategies and then distill them into more
concise reasoning traces. We augment GRPO with a reward function that balances
three signals: task correctness (via verifier feedback), length efficiency, and
formatting adherence (via structural tags). Experiments on GSM8K, MATH500,
SVAMP, College Math, and GSM+ demonstrate that curriculum-based training
consistently outperforms fixed-budget baselines at the same final budget,
achieving higher accuracy and significantly improved token efficiency. We
further ablate the impact of reward weighting and decay schedule design,
showing that progressive constraint serves as a powerful inductive bias for
training efficient reasoning models. Our code and checkpoints are released at:
https://github.com/hammoudhasan/curriculum_grpo.