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cs.AI2025
GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning
Yao Zhang, Yu Wu, Haowei Zhang +6
Process Reward Models (PRMs) aim to improve multi-step reasoning in Large Language Models (LLMs) by supervising intermediate steps and identifying errors. However, building effecti…
cs.AI2025
Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning
Sai Wang, Yu Wu, Zhongwen Xu
The pursuit of artificial agents that can learn to master complex environments has led to remarkable successes, yet prevailing deep reinforcement learning methods often rely on imm…
cs.AI2025
From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascades
Yu Wu, Shuo Wu, Ye Tao +2
Standard LLM cascades improve efficiency by deferring difficult queries from weak to strong models. However, these systems are typically static: when faced with repeated or semanti…