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cs.AI2026
Mitigating Over-Optimization in PRM-Guided Search in Mathematical Reasoning by Optimizing the Guide
Taejong Joo, Diego Klabjan
Process reward models (PRMs) provide dense step-level guidance for search-based reasoning, enabling inference-time compute to be allocated toward promising partial solutions. Howev…
cs.LG2026
On Surprising Effectiveness of Masking Updates in Adaptive Optimizers
Taejong Joo, Wenhan Xia, Cheolmin Kim +2
Training large language models (LLMs) relies almost exclusively on dense adaptive optimizers with increasingly sophisticated preconditioners. We challenge this by showing that rand…