5 papers
PEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language Models
Junxin Wang, Yingfeng Luo, Xiangchen Song +12
Effective multimodal reasoning requires a model to reliably utilize visual information while sufficiently exploring diverse reasoning paths. To achieve this goal, prior work combin…
Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning
Yifu Huo, Shunjie Xing, Chenglong Wang +8
Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aim…
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models
Yifu Huo, Chenglong Wang, Ziming Zhu +9
Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends…
HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
Yifu Huo, Chenglong Wang, Qiren Zhu +5
Preference optimization methods like DPO have achieved remarkable performance in LLM alignment. However, the evaluation for these methods relies on a single response and overlooks…
LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination
Ziming Zhu, Chenglong Wang, Haosong Xv +8
Despite the remarkable progress of modern machine translation (MT) systems on general-domain texts, translating structured LaTeX-formatted documents remains a significant challenge…