3 papers
cs.AI2026
Compositional Reasoning in Language Models under Reinforcement Learning Post-Training
Yu He, Yingxi Li, Yifei Wang +1
Compositional reasoning is critical for real-world problem solving: since training data is necessarily limited, models must generalize by composing learned skills in new ways. Whil…
cs.LG2025
Primal-Dual Neural Algorithmic Reasoning
Yu He, Ellen Vitercik
Neural Algorithmic Reasoning (NAR) trains neural networks to simulate classical algorithms, enabling structured and interpretable reasoning over complex data. While prior research…
cs.LG2025
Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures
Yu He, Yingxi Li, Colin White +1
Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithmic reasoning abilities is therefore cr…