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cs.LG2026
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation
Minsuh Joo, Youna Kim, Choonghyun Park +2
LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their outputs. To bridge this gap,…
cs.LG2026
QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning
Doyeon Lee, Eunyi Lyou, Hyunsoo Cho +3
GRPO-style reinforcement learning (RL)-based LLM fine-tuning algorithms have recently gained popularity. Relying on heuristic trust-region approximations, however, they can lead to…
cs.LG2026
Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty
Jeonghyun Kim, SooKyung Kim, Richeng Xuan +1
The core of knowledge distillation lies in transferring the teacher's rich 'dark knowledge'-subtle probabilistic patterns that reveal how classes are related and the distribution o…