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cs.LG2026
Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation
Jingang Zhou, Yuyi Zhou, Haiyang Guo +6
On-policy distillation (OPD) is a promising approach for transferring knowledge between language models, where a student receives dense token-level supervision along its own genera…
cs.LG2026
CAMFT: Conflict-Aware Mergeable Fine-Tuning for Large Language Models
Jingang Zhou, Haiyang Guo, Yuan Ma +2
Model merging has emerged as a promising paradigm for integrating multiple task-specific capabilities into a single large language model. However, existing methods predominantly fo…
cs.LG2026
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
Siqing Song, Chuang Wang, Yong Lang +2
Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBLLM, a lightweight binarization…