Composition-RL is a data-efficient Reinforcement Learning with Verifiable Rewards (RLVR) approach that addresses the challenge of "too-easy" prompts (those with a pass rate of 1) by automatically composing multiple verifiable problems into a single, harder yet still-verifiable prompt. This method ensures that training signals remain informative throughout the reinforcement learning process, leading to consistent improvements in reasoning capability.
If you find this work helpful for your research, please consider citing the paper:
1@article{xu2026composition-rl,
2 title={Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models},
3 author={Xu, Xin and Bai, Clive and Yang, Kai and Chen, Tianhao and Chen, Yangkun and Liu, Weijie and Chen, Hao and Wang, Yang and Yang, Saiyong and Yang, Can},
4 journal={arXiv preprint arXiv:2602.12036},
5 year={2026}
6}
