4 papers
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models
Canhui Wu, Qiong Cao, Chang Li +5
Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks but often suffer from excessive verbosity, known as "overthinking." Existing solutions via reinforceme…
Efficient Reasoning via Thought-Training and Thought-Free Inference
Canhui Wu, Qiong Cao, Chao Xue +2
Recent advances in large language models (LLMs) have leveraged explicit Chain-of-Thought (CoT) prompting to improve reasoning accuracy. However, most existing methods primarily foc…
ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
Wentao Tan, Qiong Cao, Chao Xue +3
The chart-to-code generation task requires MLLMs to convert chart images into executable code. This task faces two main challenges: limited data diversity and the difficulty of mai…
Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs
Chang Li, Yaren Zhang, Haoran Lv +3
Large Language Models (LLMs) have shown remarkable reasoning ability through explicit Chain-of-Thought (CoT) prompting, but generating these step-by-step textual explanations is co…