When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper
• 2402.17193 • Published
• 26
Fine tuning
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What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
Paper • 2410.23743 • Published • 64 -
Direct Preference Optimization Using Sparse Feature-Level Constraints
Paper • 2411.07618 • Published • 17 -
Transformer^2: Self-adaptive LLMs
Paper • 2501.06252 • Published • 55 -
Control LLM: Controlled Evolution for Intelligence Retention in LLM
Paper • 2501.10979 • Published • 6 -
Taming LLMs by Scaling Learning Rates with Gradient Grouping
Paper • 2506.01049 • Published • 38 -
Leveraging Self-Attention for Input-Dependent Soft Prompting in LLMs
Paper • 2506.05629 • Published • 37 -
All is Not Lost: LLM Recovery without Checkpoints
Paper • 2506.15461 • Published • 39 -
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
Paper • 2506.19767 • Published • 15 -
Optimizing ML Training with Metagradient Descent
Paper • 2503.13751 • Published • 1 -
Towards a Unified View of Large Language Model Post-Training
Paper • 2509.04419 • Published • 76 -
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
Paper • 2509.16596 • Published • 14 -
Fine-tuning Done Right in Model Editing
Paper • 2509.22072 • Published • 28 -
Interactive Training: Feedback-Driven Neural Network Optimization
Paper • 2510.02297 • Published • 43 -
LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper • 2510.18866 • Published • 114 -
π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
Paper • 2510.25889 • Published • 66 -
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
Paper • 2511.20626 • Published • 43 -
Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning
Paper • 2602.01058 • Published • 41 -
MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration
Paper • 2602.01734 • Published • 32 -
Unified Latents (UL): How to train your latents
Paper • 2602.17270 • Published • 46