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
The proliferation of large language models and the increasing focus on efficient fine-tuning techniques for various tasks, including reinforcement learning, makes understanding initialization methods critical.
This research highlights a nuanced challenge in adapting efficient fine-tuning methods like LoRA to RL-based paradigms, impacting the development and deployment of more capable AI models.
The understanding of how geometry-preserving orthonormal initialization impacts low-rank adaptation performance in RLVR changes; methods successful in SFT may not translate directly to RLVR.
- · AI researchers focusing on RL
- · Developers of custom LoRA variants
- · Users of RL with verifiable rewards
- · Developers relying solely on SFT-optimized LoRA variants for RL
- · Models with unstable RL fine-tuning
Further research will focus on developing RLVR-specific initialization strategies for low-rank adaptation.
Improved and stable fine-tuning in RLVR could lead to more robust and ethical AI agents.
The enhanced stability and efficiency in RLVR fine-tuning could accelerate the deployment of AI in sensitive autonomous systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG