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Notes on fine-tuning
6 notes
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Researchers propose a new method for fine-tuning large language models in a federated learning paradigm using Low-rank adaptation (LoRA). They investigate two strategies, Share-A/Local-B and Share-B/Local-A, and reveal…
Active Offline-to-Online Reinforcement Learning
Researchers propose an active policy selection method for fine-tuning in offline-to-online reinforcement learning (O2O-RL) settings, where the goal is to allocate a limited online interaction budget effectively among…
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
In this research, the authors propose Localized LoRA-MoE, a framework that combines localized spatial blocking with dynamic, context-conditioned routing for Large Language Models (LLMs) and high-dimensional perception…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating natural language descriptions of strategic requirements into well-formed formulas using Large Language Models (LLMs). They created and curated a novel dataset for…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study shows that Language Models (LLMs) fine-tuned for security classification may overlook vulnerabilities introduced during fine-tuning itself. These models can learn token-level indicator semantics that preserve…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
A study shows that Language Models (LLMs) fine-tuned for security classification can miss vulnerabilities due to learning token-level indicator semantics that preserve accuracy on their training data but fail under…
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