11 citations · 20 across the 14 of their papers we have counts for
6 papers · 1 filter
Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
Guofu Xie, Chen Zhang, Xiao Zhang +3
Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indir…
CAPO: Towards Enhancing LLM Reasoning through Generative Credit Assignment
Guofu Xie, Yunsheng Shi, Hongtao Tian +2
Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of Large Language Models (LLMs) by using rule-based binary feedback. However, current RLV…
Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation
Guofu Xie, Xiao Zhang, Ting Yao +1
User information needs are often highly diverse and varied. A key challenge in current research is how to achieve controllable multi-objective generation while enabling rapid adapt…
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
Advik Raj Basani, Xiao Zhang
LLMs have shown impressive capabilities across various natural language processing tasks, yet remain vulnerable to input prompts, known as jailbreak attacks, carefully designed to…
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Yifan Zeng, Yiran Wu, Xiao Zhang +2
Despite extensive pre-training in moral alignment to prevent generating harmful information, large language models (LLMs) remain vulnerable to jailbreak attacks. In this paper, we…
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang, Hang Yu, Junqiao Zhao +5
Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high couplin…