2 citations · 2 across the 5 of their papers we have counts for
5 papers
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
Tianhao Hu, Xiangcheng Liu, Yuchun Miao +16
Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed ge…
CoBA-RL: Capability-Oriented Budget Allocation for Reinforcement Learning in LLMs
Zhiyuan Yao, Yi-Kai Zhang, Yuxin Chen +7
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning. However, standard frameworks like Group Relative Policy Optimizatio…
Self-playing Adversarial Language Game Enhances LLM Reasoning
Pengyu Cheng, Tianhao Hu, Han Xu +6
We explore the potential of self-play training for large language models (LLMs) in a two-player adversarial language game called Adversarial Taboo. In this game, an attacker and a…
On Diversified Preferences of Large Language Model Alignment
Dun Zeng, Yong Dai, Pengyu Cheng +5
Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality. However, in this pluralistic world, human prefer…
Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game
Pengyu Cheng, Yifan Yang, Jian Li +5
Human preference alignment is essential to improve the interaction quality of large language models (LLMs). Existing alignment methods depend on manually annotated preference data…