28 citations · 38 across the 15 of their papers we have counts for
5 papers · 1 filter
Defeating the Training-Inference Mismatch via FP16
Penghui Qi, Zichen Liu, Xiangxin Zhou +4
Reinforcement learning (RL) fine-tuning of large language models (LLMs) often suffers from instability due to the numerical mismatch between the training and inference policies. Wh…
SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
Bo Liu, Leon Guertler, Simon Yu +9
Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approache…
Optimizing Anytime Reasoning via Budget Relative Policy Optimization
Penghui Qi, Zichen Liu, Tianyu Pang +3
Scaling test-time compute is crucial for enhancing the reasoning capabilities of large language models (LLMs). Existing approaches typically employ reinforcement learning (RL) to m…
Understanding R1-Zero-Like Training: A Critical Perspective
Zichen Liu, Changyu Chen, Wenjun Li +5
DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning. In this work, we critic…
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
Xinyi Wan, Penghui Qi, Guangxing Huang +2
Pipeline parallelism (PP) is widely used for training large language models (LLMs), yet its scalability is often constrained by high activation memory consumption as the number of…