3 papers
cs.CL2026
Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning
Futing Wang, Jianhao Yan, Yun Luo +6
Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine multiple reasoning hypotheses wit…
cs.CL2025
Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
Bowen Ding, Yuhan Chen, Futing Wang +2
Large Reasoning Models (LRMs) excel at solving complex problems but face an overthinking dilemma. When handling simple tasks, they often produce verbose responses overloaded with t…
cs.CL2025
ELICIT: LLM Augmentation via External In-Context Capability
Futing Wang, Jianhao Yan, Yue Zhang +1
Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application. Traditional fine-tuning methods require substantial data and co…