45 citations · 204 across the 37 of their papers we have counts for
11 papers · 1 filter
GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
Ruida Wang, Jiarui Yao, Rui Pan +2
Solving math problems through verifiable languages such as Lean has significantly impacted both the mathematics and computer science communities. Current state-of-the-art models ar…
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Rui Pan, Shivanshu Shekhar, Boyao Wang +5
Small language models (SLMs) have attracted considerable attention from both academia and industry due to their broad range of applications in edge devices. To obtain SLMs with str…
Entropy-Regularized Process Reward Model
Hanning Zhang, Pengcheng Wang, Shizhe Diao +6
Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning, often making systematic error…
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Haoxiang Wang, Yong Lin, Wei Xiong +5
Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human…
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
Rui Pan, Xiang Liu, Shizhe Diao +4
The machine learning community has witnessed impressive advancements since large language models (LLMs) first appeared. Yet, their massive memory consumption has become a significa…
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal +7
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcome…
Normalizing Flow with Variational Latent Representation
Hanze Dong, Shizhe Diao, Weizhong Zhang +1
Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions. However, the standar…
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
Xiao Zhou, Weizhong Zhang, Zonghao Chen +2
Sparse training is a natural idea to accelerate the training speed of deep neural networks and save the memory usage, especially since large modern neural networks are significantl…