10 citations · 43 across the 22 of their papers we have counts for
23 papers
Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs
Jiacheng Xu, Wentao Zhang, Zhiyi Lyu +4
Reinforcement learning (RL) has substantially advanced code generation with large language models (LLMs) through executable feedback. The feedback for coding problems mainly comes…
Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao +9
Despite the critical role of reward models (RMs) in Reinforcement Learning from Human Feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation…
Incentivizing LLMs to Self-Verify Their Answers
Fuxiang Zhang, Jiacheng Xu, Chaojie Wang +3
Large Language Models (LLMs) have demonstrated remarkable progress in complex reasoning tasks through both post-training and test-time scaling laws. While prevalent test-time scali…
Skywork Open Reasoner 1 Technical Report
Jujie He, Jiacai Liu, Chris Yuhao Liu +14
The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we…
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization
Jiacai Liu, Chaojie Wang, Chris Yuhao Liu +5
The role of reinforcement learning (RL) in enhancing the reasoning of large language models (LLMs) is becoming increasingly significant. Despite the success of RL in many scenarios…
Mars-PO: Multi-Agent Reasoning System Preference Optimization
Xiaoxuan Lou, Chaojie Wang, Bo An
Mathematical reasoning is a fundamental capability for large language models (LLMs), yet achieving high performance in this domain remains a significant challenge. The auto-regress…
Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs
Chris Yuhao Liu, Liang Zeng, Jiacai Liu +6
In this report, we introduce a collection of methods to enhance reward modeling for LLMs, focusing specifically on data-centric techniques. We propose effective data selection and…
Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks
Chaojie Wang, Xinyang Liu, Dongsheng Wang +3
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to appr…
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
Yewen Li, Chaojie Wang, Xiaobo Xia +6
Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a detector trained solely on unlabeled in-distribution (ID) data. The likelihood function es…
Disentangled Generative Graph Representation Learning
Xinyue Hu, Zhibin Duan, Xinyang Liu +6
Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representa…
Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
Chaojie Wang, Yanchen Deng, Zhiyi Lyu +4
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. However, the auto-regressive generation process makes LLMs prone to produce err…
Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
Zitao Song, Chao Yang, Chaojie Wang +2
Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool to elicit logic tr…
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
Chuqiao Zong, Chaojie Wang, Molei Qin +3
High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative t…
keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM
Chaojie Wang, Yishi Xu, Zhong Peng +5
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. However, in the face of pr…
Knowledge-Aware Bayesian Deep Topic Model
Dongsheng Wang, Yishi Xu, Miaoge Li +4
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Although embedded topic models (ETMs) and its variants have gained…
Ordinal Graph Gamma Belief Network for Social Recommender Systems
Dongsheng Wang, Chaojie Wang, Bo Chen +1
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships betwee…
MDMLP: Image Classification from Scratch on Small Datasets with MLP
Tian Lv, Chongyang Bai, Chaojie Wang
The attention mechanism has become a go-to technique for natural language processing and computer vision tasks. Recently, the MLP-Mixer and other MLP-based architectures, based sim…
A Variational Edge Partition Model for Supervised Graph Representation Learning
Yilin He, Chaojie Wang, Hao Zhang +2
Graph neural networks (GNNs), which propagate the node features through the edges and learn how to transform the aggregated features under label supervision, have achieved great su…
TopicNet: Semantic Graph-Guided Topic Discovery
Zhibin Duan, Yishi Xu, Bo Chen +3
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic…
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
Yongrui Chen, Xinnan Guo, Chaojie Wang +4
Single-table text-to-SQL aims to transform a natural language question into a SQL query according to one single table. Recent work has made promising progress on this task by pre-t…
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen +5
Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic…
Convolutional Poisson Gamma Belief Network
Chaojie Wang, Bo Chen, Sucheng Xiao +1
For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector. In this pape…
Nonparametric Statistical Inference and Imputation for Incomplete Categorical Data
Chaojie Wang, Linghao Shen, Han Li +1
Missingness in categorical data is a common problem in various real applications. Traditional approaches either utilize only the complete observations or impute the missing data by…