1 citations · 1 across the 9 of their papers we have counts for
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Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis
Jiatong Li, Weida Wang, Changmeng Zheng +4
Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid…
MarkovScale: Towards Optimal Sequential Scaling at Inference Time
Youkang Wang, Jian Wang, Rubing Chen +3
Sequential scaling is a prominent inference-time scaling paradigm, yet its performance improvements are typically modest and not well understood, largely due to the prevalence of h…
OptPO: Optimal Rollout Allocation for Test-time Policy Optimization
Youkang Wang, Jian Wang, Rubing Chen +3
Test-time policy optimization enables large language models (LLMs) to adapt to distribution shifts by leveraging feedback from self-generated rollouts. However, existing methods re…
OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen +1
Inference-time scaling has emerged as a powerful technique for enhancing the reasoning performance of Large Language Models (LLMs). However, existing approaches often rely on heuri…