Fine tuning
updated
When Scaling Meets LLM Finetuning: The Effect of Data, Model and
Finetuning Method
Paper
•
2402.17193
•
Published
•
26
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
•
54
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
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2506.05629
•
Published
•
37
All is Not Lost: LLM Recovery without Checkpoints
Paper
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2506.15461
•
Published
•
38
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
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2509.04419
•
Published
•
75
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
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2509.22072
•
Published
•
28
Interactive Training: Feedback-Driven Neural Network Optimization
Paper
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2510.02297
•
Published
•
42
LightMem: Lightweight and Efficient Memory-Augmented Generation
Paper
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2510.18866
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Published
•
111
π_RL: Online RL Fine-tuning for Flow-based
Vision-Language-Action Models
Paper
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2510.25889
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Published
•
65
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
Paper
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2511.20626
•
Published
•
43