Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning explores This paper explores a novel learning rate scheduling method for pre-training large language models to enhance their adaptability in downstream tasks.. Commercial viability score: 2/10 in LLM Training.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkWhat do near-optimal learning rate schedules look like?Viability: 2.0
- Prior WorkOn Catastrophic Forgetting in Low-Rank Decomposition-Based Parameter-Efficient Fine-TuningViability: 2.0
- Higher ViabilityWeight Decay Improves Language Model PlasticityViability: 4.0
- Higher ViabilityBeyond Precision: Training-Inference Mismatch is an Optimization Problem and Simple LR Scheduling Fixes ItViability: 6.0
- Higher ViabilityAnytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging