4 papers · 1 filter
CALIBUDGET: Calibration-Guided Source Allocation for Fixed-Budget Mixed-Reasoning Adaptation
Yupeng Chang, Yuan Wu
Fixed-budget adaptation from heterogeneous data sources requires deciding not only how much data to use, but how much exposure each source receives. Size-proportional rules can cro…
Learned Reporting Preferences in RLVR Can Conflict with the Current Request
Yupeng Chang, Wenxuan Zhang, Yuan Wu
Reinforcement learning with verifiable rewards (RLVR) has become a prominent approach for improving language-model performance on reasoning tasks using automatically checked answer…
From Checkpoint Variation to Selection Gains in Supervised Fine-Tuning
Yupeng Chang, Wenxuan Zhang, Yuan Wu
Checkpoint selection is a routine decision in supervised fine-tuning (SFT): training produces multiple checkpoints, but only one is retained. Yet fixed-budget comparisons do not by…
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
Yupeng Chang, Yuan Wu, Yi Chang
Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models. Under a fixed rank budget, LoRA parameterizes each adapted weig…