1 citations · 1 across the 4 of their papers we have counts for
4 papers
Deep Research as Rubric for Reinforcement Learning
Wangyi Mei, Zhouhong Gu, Zhenhan Bai +9
Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but ex…
RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs
Runlong Zhou, Lefan Zhang, Shang-Chen Wu +29
Reinforcement learning (RL) has emerged as the de-facto paradigm for improving the reasoning capabilities of large language models (LLMs). We have developed RLAX, a scalable RL fra…
Learning Harmonized Representations for Speculative Sampling
Lefan Zhang, Xiaodan Wang, Yanhua Huang +1
Speculative sampling is a promising approach to accelerate the decoding stage for Large Language Models (LLMs). Recent advancements that leverage target LLM's contextual informatio…
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Lefan Zhang, Zhang-Hao Tian, Wujun Zhou +1
The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been…