9 citations · 16 across the 14 of their papers we have counts for
14 papers
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
Xiangkun Sun, Lingkai Kong, Aoqi Zhang +2
Large language models can answer a factual question correctly, yet switch to an incorrect answer when exposed to persuasive context. Why this happens inside the model remains uncle…
LLM Advertisement based on Neuron Auctions
Peiran Yun, Wenxin Xu, Jiayuan Liu +4
As Large Language Models (LLMs) transition into conversational agents, generative advertising emerges as a crucial monetization strategy. However, embedding advertisements within u…
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…
Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs
Liang Zeng, Yongcong Li, Yuzhen Xiao +8
Software engineering (SWE) has recently emerged as a crucial testbed for next-generation LLM agents, demanding inherent capabilities in two critical dimensions: sustained iterative…
AgentOrchestra: Orchestrating Multi-Agent Intelligence with the Tool-Environment-Agent(TEA) Protocol
Wentao Zhang, Liang Zeng, Yuzhen Xiao +7
Recent advances in LLM-based agent systems have shown promise on complex, long-horizon tasks, but existing agent protocols (e.g., A2A and MCP) do not adequately support lifecycle-a…
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…
SAT-LDM: Provably Generalizable Image Watermarking for Latent Diffusion Models with Self-Augmented Training
Lu Zhang, Liang Zeng
The rapid proliferation of AI-generated images necessitates effective watermarking techniques to protect intellectual property and detect fraudulent content. While existing trainin…
Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
Xinyi Yang, Liang Zeng, Heng Dong +6
As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexi…
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…
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…
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…
Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On
Liang Zeng, Liangjun Zhong, Liang Zhao +9
In this paper, we investigate the underlying factors that potentially enhance the mathematical reasoning capabilities of large language models (LLMs). We argue that the data scalin…
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
Tianwen Wei, Bo Zhu, Liang Zhao +13
In this technical report, we introduce the training methodologies implemented in the development of Skywork-MoE, a high-performance mixture-of-experts (MoE) large language model (L…
LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models
Liang Zhao, Tianwen Wei, Liang Zeng +12
We introduce LongSkywork, a long-context Large Language Model (LLM) capable of processing up to 200,000 tokens. We provide a training recipe for efficiently extending context lengt…