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cs.AI2025
Lean-STaR: Learning to Interleave Thinking and Proving
Haohan Lin, Zhiqing Sun, Sean Welleck +1
Traditional language model-based theorem proving assumes that by training on a sufficient amount of formal proof data, a model will learn to prove theorems. Our key observation is…
cs.AI2025
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
Yangzhen Wu, Zhiqing Sun, Shanda Li +2
While the scaling laws of large language models (LLMs) training have been extensively studied, optimal inference configurations of LLMs remain underexplored. We study inference sca…
cs.AI2024
Improve Vision Language Model Chain-of-thought Reasoning
Ruohong Zhang, Bowen Zhang, Yanghao Li +6
Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT r…