Composition-RL is a data-efficient Reinforcement Learning with Verifiable Rewards (RLVR) approach introduced in the paper:
Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models.
Composition-RL addresses the challenge of "too-easy" prompts (where the pass rate becomes 1 during training) by automatically composing multiple verifiable problems into a single, more complex, yet still verifiable prompt. This ensures that the model continues to receive informative training signals throughout the RL process, leading to improved reasoning capabilities across mathematical and scientific domains.
For implementation details, including data generation and evaluation scripts, please refer to the
official GitHub repository.
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}
