arxiv
PublishedJune 11, 2026 at 4:00 AM
—neutral
Compatibility-Aware Dynamic Fine-Tuning for Large Language Models
Publisher summary· verbatim
arXiv:2606.11206v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization. Recent work attributes this issue to pathological gradient scaling and proposes
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning12harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!12harxivARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation12harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions12hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗