Researchers from the Broad Institute, University of Tübingen, and Harvard University are shifting the focus of large language model (LLM) development from merely optimizing initial performance to enhancing adaptability, a concept they term “plasticity.” Tessa Han of the Broad Institute, along with colleagues Schmidt Bordt, Hanlin Zhang, and Sham Kakade, conducted a study demonstrating that increased weight decay during pretraining significantly improves a model’s adaptability in fine-tuning for specific downstream tasks. This finding highlights an important trade-off: models that may initially seem less capable can ultimately outperform their peers when adequately fine-tuned.
The research underscores a critical shift in understanding LLM performance metrics, moving beyond the traditional emphasis on minimizing cross-entropy loss to considering a model’s ability to learn new tasks efficiently. By investigating the impact of weight decay on the Llama-2 and OLMo-2 model families, the team discovered that higher weight decay values foster improved task performance during fine-tuning, even when initial pretraining metrics appear suboptimal.
Through systematic experimentation, the researchers evaluated models ranging from 0.5 billion to 4 billion parameters under various training conditions. They pre-trained these models using two distinct token-per-parameter (TPP) regimes: a compute-optimal 20 TPP and an overtrained 140 TPP. The findings revealed that, at 20 TPP, models like Llama-2-0.5B-20x and Llama-2-1B-20x achieved optimal validation loss with a weight decay of 0.5, while Llama-2-4B-20x reached its lowest loss at a higher weight decay of 1.0. Conversely, in the overtrained regime, the OLMo-2-1B-140x model performed best with a default weight decay of 0.1, reflecting a departure from the earlier trend.
Further analysis demonstrated that models pretrained with elevated weight decay values consistently outperformed their counterparts in fine-tuning assessments across six Chain-of-Thought tasks. Specifically, models in the 20 TPP regime that employed a weight decay of 1.0 showed remarkable gains in downstream task performance, thereby challenging the conventional wisdom that prioritizes low pretraining loss as the sole indicator of model efficacy.
Historically, the industry has often equated larger model sizes with improved task performance. However, this research illustrates that merely scaling LLMs does not guarantee enhanced adaptability. By emphasizing the importance of plasticity, the study compels researchers to rethink their training strategies, noting that a model’s internal representations can be significantly influenced by weight decay. While higher weight decay may lead to lower initial performance, it fosters a model’s ability to adapt to new challenges, thereby necessitating a more nuanced evaluation of model quality.
As scientists continue to explore the implications of this research, the focus will likely shift toward developing a more holistic understanding of pretraining strategies. This understanding should prioritize not only immediate performance metrics but also the potential for future adaptability in real-world applications. The findings from this study underscore the necessity for a reevaluation of hyperparameter choices in LLM training, suggesting that optimizing for plasticity could unlock substantial performance gains across various tasks.
In summary, the collaborative work of researchers across multiple prestigious institutions marks a pivotal moment in LLM development. By emphasizing adaptability through specific training techniques such as weight decay, this research lays the groundwork for future innovations in language modeling that prioritize not just initial success but the ability to evolve and adapt in response to new demands.
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