8 papers
On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
Sunghwan Kim, Junhee Cho, Beong-woo Kwak +6
Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While prior work has primarily focuse…
Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
Wen Luo, Guangyue Peng, Liang Wang +7
Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning s…
Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning
Jiebin Zhang, Zhenghan Yu, Liang Wang +8
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…
QueST: Incentivizing LLMs to Generate Difficult Problems
Hanxu Hu, Xingxing Zhang, Jannis Vamvas +2
Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labele…
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
Yiyao Yu, Yuxiang Zhang, Dongdong Zhang +9
Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet often rely on single-paradigm reasoning, limiting their effectiveness across diverse tasks. W…
Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching
Wenxiao Cai, Dongting Hu, Ruoyan Yin +4
Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability. Despite this, the estimation and analysis of…