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cs.LG2026
RVPO: Risk-Sensitive Alignment via Variance Regularization
Ivan Montero, Tomasz Jurczyk, Bhuwan Dhingra
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective ca…
cs.LG2026
Over-Searching in Search-Augmented Large Language Models
Roy Xie, Deepak Gopinath, David Qiu +4
Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search -- unnecessarily invoking search…
cs.LG2025
When Greedy Wins: Emergent Exploitation Bias in Meta-Bandit LLM Training
Sanxing Chen, Xiaoyin Chen, Yukun Huang +2
While Large Language Models (LLMs) hold promise to become autonomous agents, they often explore suboptimally in sequential decision-making. Recent work has sought to enhance this c…