2 papers
cs.LG2026
Diagnosing Capability Gaps in Fine-Tuning Data
Saeid Asgari Taghanaki, Rakshanda Agarwal, Bruce Sun +10
Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a practitioner needs. Yet identifyi…
cs.CL2026
SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, Guilherme Potje, Shivam Shandilya +9
Designing aligned and robust rewards for open-ended generation remains a key barrier to RL post-training. Rubrics provide structured, interpretable supervision, but scaling rubric…