DenoiseRL is a reinforcement learning framework that replaces external supervision with recovery-oriented optimization over failures from weak models. It learns directly from incorrect reasoning traces by conditioning the policy on noisy prefixes and optimizing the on-policy continuation, improving reasoning performance, exploration efficiency, and self-corrective behavior without stronger teacher models or heavily curated difficult datasets.
@article{xu2026denoiserl,title={{DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes}},author={Xu, Caijun and Xiao, Changyi and Peng, Zhongyuan and Cao, Yixin},journal={arXiv preprint arXiv:2605.28421},year={2026},month=may,url={https://arxiv.org/abs/2605.28421},}
ACL’26 Findings
SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning
Caijun Xu, Changyi Xiao, Zhongyuan Peng, Xinrun Wang, and Yixin Cao
In Findings of the Association for Computational Linguistics: ACL 2026, May 2026
@inproceedings{xu2026scaler,title={{SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning}},author={Xu, Caijun and Xiao, Changyi and Peng, Zhongyuan and Wang, Xinrun and Cao, Yixin},booktitle={Findings of the Association for Computational Linguistics: {ACL} 2026},year={2026},url={https://arxiv.org/abs/2601.04809},}
2024
CIKM’24
Exploring High-Order User Preference with Knowledge Graph for Recommendation
Caijun Xu, Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, and Rui Liu
In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, May 2024
@inproceedings{xu2024exploring,title={{Exploring High-Order User Preference with Knowledge Graph for Recommendation}},author={Xu, Caijun and Zhang, Fuwei and Zhang, Zhao and Zhuang, Fuzhen and Liu, Rui},booktitle={Proceedings of the 33rd {ACM} International Conference on Information and Knowledge Management},pages={4138--4142},year={2024},url={https://doi.org/10.1145/3627673.3679921},}