1 citations · 1 across the 4 of their papers we have counts for
5 papers
DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning
Yaxuan Wang, Chris Yuhao Liu, Quan Liu +4
Unlearning in Large Language Models (LLMs) is crucial for protecting private data and removing harmful knowledge. Most existing approaches rely on fine-tuning to balance unlearning…
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…
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…
GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection
Zhijie Deng, Chris Yuhao Liu, Zirui Pang +5
Large Language Models (LLMs) have demonstrated strong capabilities in memorizing vast amounts of knowledge across diverse domains. However, the ability to selectively forget specif…
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…