Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR

arXiv:2606.31813v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) paradigm. However, their efficacy and behavior under Reinforcement learning with verifiable rewards (RLVR) are less well understood. In particular, two structurally initialized LoRA variants, PiSSA and MiLoRA, which outperform standard LoRA under SFT, can underperform standard LoRA under RLVR and may even exhibit training instability. These observations suggest that how to initialize the low-rank matri

Source: arXiv cs.LG — read the full report at the original publisher.

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