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#artificial intelligence Preprint Sep 2026

Rational Clarification by Assistive Agents via Value-of-Information Reasoning

Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --- or ask a clarifying question. Which option is the most safe and helpful? A common approach is to ask questions that mi...

T. Nguyen-Hien, Y. Teh, Wee-Sun Lee et al. · 0 citations
#machine learning Preprint Aug 2026

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its heavyweight backpropagation-based training stack makes it impractical to fine-tune larger LLMs...

Zhi Zheng, Rong-Sheng Chen, Yunpeng Ba et al. · 1 citation

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