4 papers
Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning
Shaojie Wang, Liang Zhang
Current plan-based reasoning methods improve large language models (LLMs) by inserting a planning stage before execution, giving rise to the question plan $\rightarro…
From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning
Shaojie Wang, Liang Zhang
Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcement Learning (RL). While effecti…
From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs
Shaojie Wang, Liang Zhang
Recent studies reveal that large language models (LLMs) exhibit limited logical reasoning abilities in mathematical problem-solving, instead often relying on pattern-matching and m…
Rethinking the Understanding Ability across LLMs through Mutual Information
Shaojie Wang, Sirui Ding, Na Zou
Recent advances in large language models (LLMs) have revolutionized natural language processing, yet evaluating their intrinsic linguistic understanding remains challenging. Moving…