Agentic RL Training Recipes

Training recipes, reward hacking, data mixing

Reinforcement learning is now the main lever for turning a strong base model into a capable agent. I work on the full stack of that process: training recipes for reasoning, planning, tool use, and agentic coding; reward modeling and rubric-based rewards, including their failure modes such as reward hacking (Yang et al., 2026); RL recipes that resist reward hacking and efficient multi-agent systems; and the asynchronous, high-throughput RL systems that make long-horizon agent training practical.

References

2026

arXiv

Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL

Minglai Yang, Xinyu Guo, Utkarsh Tyagi, Mian Zhang, Razvan Dumitru, Sunjie Hou, Yunzhong He, Daniel Yue Zhang, and Ying Liu

arXiv preprint arXiv:2608.11669, Aug 2026

arXiv Bib

@article{yang2026rubric,
  title = {Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward {RL}},
  author = {Yang, Minglai and Guo, Xinyu and Tyagi, Utkarsh and Zhang, Mian and Dumitru, Razvan and Hou, Sunjie and He, Yunzhong and Zhang, Daniel Yue and Liu, Ying},
  journal = {arXiv preprint arXiv:2608.11669},
  year = {2026},
  month = aug,
}