8 papers
SPEC-RL: Accelerating On-Policy Reinforcement Learning with Speculative Rollouts
Bingshuai Liu, Ante Wang, Zijun Min +7
Large Language Models (LLMs) increasingly rely on reinforcement learning with verifiable rewards (RLVR) to elicit reliable chain-of-thought reasoning. However, the training process…
Orchestrating Tokens and Sequences: Dynamic Hybrid Policy Optimization for RLVR
Zijun Min, Bingshuai Liu, Ante Wang +4
Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising framework for optimizing large language models in reasoning tasks. However, existing RLVR algorithms focus…
Can LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation
Meiman Xiao, Ante Wang, Qingguo Hu +5
Precisely controlling the length of generated text is a common requirement in real-world applications. However, despite significant advancements in following human instructions, La…
BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models
Yujie Lin, Jiayao Ma, Qingguo Hu +5
Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation. Current fairness interventions often adopt a difference-…
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models
Hongyao Tu, Liang Zhang, Yujie Lin +4
The goal of open relation extraction (OpenRE) is to develop an RE model that can generalize to new relations not encountered during training. Existing studies primarily formulate O…
Boosting Visual Knowledge-Intensive Training for LVLMs Through Causality-Driven Visual Object Completion
Qingguo Hu, Ante Wang, Jia Song +3
Large Vision-Language Models (LVLMs) have experienced significant advancements in recent years. However, their performance still falls short in tasks requiring deep visual percepti…