9 papers
Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning
Lang Cao
Aligned large language models (LLMs) are expected to exhibit safety behavior based on the content of the user request: they should refuse unsafe requests and comply with safe ones.…
Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
Lang Cao, Renhong Chen, Luyi Li +3
Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control. However,…
SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management
Shengyue Guan, Yihao Liu, Lang Cao
Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supply chain management. However, su…
TableMaster: A Recipe to Advance Table Understanding with Language Models
Lang Cao, Hanbing Liu
Tables serve as a fundamental format for representing structured relational data. While current language models (LMs) excel at many text-based tasks, they still face challenges in…
TreeAdv: Tree-Structured Advantage Redistribution for Group-Based RL
Lang Cao, Hui Ruan, Yongqian Li +5
Reinforcement learning with group-based objectives, such as Group Relative Policy Optimization (GRPO), is a common framework for aligning large language models on complex reasoning…
More Bang for the Buck: Process Reward Modeling with Entropy-Driven Uncertainty
Lang Cao, Renhong Chen, Yingtian Zou +9
We introduce the Entropy-Driven Uncertainty Process Reward Model (EDU-PRM), a novel entropy-driven training framework for process reward modeling that enables dynamic, uncertainty-…