Summary of Smoltulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in Slms, by Sultan Alrashed
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs
by Sultan Alrashed
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an instruction-tuned language model called SmolTulu-1.7b-Instruct, which enhances Huggingface’s SmolLM2-1.7B base model using AllenAI’s Tulu 3 post-training pipeline. The researchers conduct comprehensive empirical analysis on a 135M parameter model and find that the relationship between learning rate and batch size significantly impacts model performance in a task-dependent manner. They discover a clear split in optimal performance ratios for reasoning tasks like ARC and GSM8K, which benefit from higher learning rates, versus pattern recognition tasks like HellaSwag and IFEval, which show optimal performance with lower ratios. These insights inform the development of SmolTulu, achieving state-of-the-art performance among sub-2B parameter models on instruction following and mathematical reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special language model that helps small computers learn to understand instructions better. They tested many combinations of settings to find the best way to make the model work well for different types of tasks. They found that some tasks, like solving math problems or understanding what someone is saying, do better with certain settings, while other tasks, like recognizing patterns, do better with different settings. This new model is called SmolTulu and it’s really good at doing these tasks. |
Keywords
» Artificial intelligence » Language model » Pattern recognition
