CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.
Lora: Low-rank adaptation of large language models
5 Pith papers cite this work. Polarity classification is still indexing.
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A 12B-parameter VLM learns to synthesize executable Behavior Tree policies from multimodal inputs via synthetic neuro-symbolic supervision, achieving zero-shot real-world transfer on robotic manipulators.
SWaRL trains code LLMs with RL using compiler correctness signals and a confidential verifier reward to embed robust, functionality-preserving watermarks that resist refactoring attacks.
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
A data curation pipeline using diffusion-generated synthetic images improves pose estimation when added to real data but underperforms when used without real anchors.
citing papers explorer
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Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems
CDWF achieves 90-99% of full fine-tuning performance with up to 120x fewer trainable parameters by dynamically allocating full trainability to gradient-important blocks and LoRA to others for PV cyberattack transfer learning.
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Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision
A 12B-parameter VLM learns to synthesize executable Behavior Tree policies from multimodal inputs via synthetic neuro-symbolic supervision, achieving zero-shot real-world transfer on robotic manipulators.
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SWaRL: Safeguard Code Watermarking via Reinforcement Learning
SWaRL trains code LLMs with RL using compiler correctness signals and a confidential verifier reward to embed robust, functionality-preserving watermarks that resist refactoring attacks.
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SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
SplitFT adapts cut-layer selection and reduces LoRA rank per client in federated split learning to improve efficiency and performance when fine-tuning LLMs on heterogeneous devices and data.
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A Real-Calibrated Synthetic-First Data Engine
A data curation pipeline using diffusion-generated synthetic images improves pose estimation when added to real data but underperforms when used without real anchors.