3 papers
cs.AI2025
Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li +3
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research ha…
cs.AI2025
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
Zongqian Wu, Tianyu Li, Baoduo Xu +4
Deep iterative chain-of-thought (CoT) reasoning enables LLMs to tackle complex tasks by progressively activating relevant pre-trained knowledge. However, it faces challenges in ens…
cs.CL2024
Rethinking Chain-of-Thought from the Perspective of Self-Training
Zongqian Wu, Baoduo Xu, Ruochen Cui +3
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent capabilities in LLMs. Interestingly, we observe that both CoT reasoning and self-trainin…