5 papers
To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
Haoqing Wang, Xiang Long, Ziheng Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level…
Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models
Hui Wu, Hengyi Cai, Jinman Zhao +6
Preference-based alignment is pivotal for training large reasoning models; however, standard methods like Direct Preference Optimization (DPO) typically treat all preference pairs…
Efficient Thought Space Exploration Through Strategic Intervention
Ziheng Li, Hengyi Cai, Xiaochi Wei +4
While large language models (LLMs) demonstrate emerging reasoning capabilities, current inference-time expansion methods incur prohibitive computational costs by exhaustive samplin…
Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding
Ziheng Li, Zexu Sun, Jinman Zhao +8
Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, existing RLV…
Keyword-Centric Prompting for One-Shot Event Detection with Self-Generated Rationale Enhancements
Ziheng Li, Zhi-Hong Deng
Although the LLM-based in-context learning (ICL) paradigm has demonstrated considerable success across various natural language processing tasks, it encounters challenges in event…