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cs.CL2025
Timber: Training-free Instruct Model Refining with Base via Effective Rank
Taiqiang Wu, Runming Yang, Tao Liu +3
Post-training, which elicits a pretrained Base model into the corresponding Instruct model, is widely considered to be superficial. In this work, we first reinforce this hypothesis…
cs.CL2025
Shadow-FT: Tuning Instruct Model via Training on Paired Base Model
Taiqiang Wu, Runming Yang, Jiayi Li +4
Large language models (LLMs) consistently benefit from further fine-tuning on various tasks. However, we observe that directly tuning the Instruct (i.e., instruction-tuned) models…
cs.CL2025
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
Zhen Li, Yupeng Su, Runming Yang +5
Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements prese…