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cs.LG2026
Smaller Models, Better Rejects: Preference Distillation Scaling
Rui Cai, Wenhui Zhu, Xiwen Chen +15
Preference distillation typically treats a teacher response as preferred and the student's own response as rejected. This assumes that self-generated failures are the most informat…
cs.LG2026
Curriculum Learning-Guided Progressive Distillation in Large Language Models
Jincheng Cao, Fanzhi Zeng, Leqi Liu +1
Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models. Existing distillation appro…
cs.LG2026
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
Shuozhe Li, Jincheng Cao, Bodun Hu +3
Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while un…