Aioli: A Unified Optimization Framework for Language Model Data Mixing (2411.05735v2)

Abstract: LLM performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout training. Surprisingly, we find that no existing method consistently outperforms a simple stratified sampling baseline in terms of average test perplexity. To understand this inconsistency, we unify existing methods into a standard framework, showing they are equivalent to solving a common optimization problem: minimize average loss subject to a method-specific mixing law -- an implicit assumption on the relationship between loss and mixture proportions. This framework suggests that measuring the fidelity of a method's mixing law can offer insights into its performance. Empirically, we find that existing methods set their mixing law parameters inaccurately, resulting in the inconsistent mixing performance we observe. Using this insight, we derive a new online method named Aioli, which directly estimates the mixing law parameters throughout training and uses them to dynamically adjust proportions. Aioli outperforms stratified sampling on 6 out of 6 datasets by an average of 0.27 test perplexity points, whereas existing methods fail to consistently beat stratified sampling, doing up to 6.9 points worse. Moreover, in a practical setting where proportions are learned on shorter runs due to computational constraints, Aioli can dynamically adjust these proportions over the full training run, consistently improving performance over existing methods by up to 12.012 test perplexity points.

• The paper introduces Aioli, an online optimization framework that dynamically adjusts data mixing strategies to reduce test perplexity.
• It unifies diverse data mixing methods under the linear mixing optimization framework, clarifying their loss-proportion relationships.
• Empirical results demonstrate that Aioli outperforms traditional stratified sampling, offering practical benefits for language model training.

An Expert Overview of "Aioli: A Unified Optimization Framework for LLM Data Mixing"

In the paper of LLMs (LMs), the effective mixture of training data from various domains, such as code, literature, and chat logs, is essential for optimizing model performance across diverse tasks. The paper "Aioli: A Unified Optimization Framework for LLM Data Mixing" investigates multiple methods for determining these optimal data mixtures, proposing a novel unified framework called Linear Mixing Optimization (LMO). This work critiques and refines previous approaches, ultimately introducing an improved data mixing method named Aioli.

Key Contributions and Findings

The paper argues that existing data mixing methods, which can be quite diverse in their approach, do not consistently surpass a baseline established through simple stratified sampling. To address this, the authors examine various methods such as offline methods that use regression models across several training runs, and online methods that adjust mixture proportions dynamically during training based on model performance metrics like loss and gradients.

To asses these previous methods, the authors manage to consolidate them under the umbrella of the LMO framework. This general framework clarifies that all mixing methods attempt to minimize the total loss while operating under specific mixing laws describing how loss varies with mixture proportions. However, the paper finds that while the parameterization of these mixing laws is empirically capable of capturing the loss-proportion relationship, the historical methods often inaccurately set these parameters, leading to inconsistent performance.

Aioli, the novel approach introduced in the paper, represents a significant advancement over these traditional methods. It utilizes an online learning strategy, estimating the mixing law parameters iteratively during model training. This dynamic parameter estimation allows Aioli to adjust mixture proportions on-the-fly, providing a more accurate and robust approach to data mixing. Importantly, Aioli demonstrated superior test perplexity performance across all tested datasets compared to stratified sampling, where previous methodologies did not.

Practical and Theoretical Implications

From a practical standpoint, Aioli can lead to more effective LLMs due to its holistic approach to leveraging varied data domains. This capability is increasingly pivotal as LMs are applied to tasks with diverse linguistic requirements. Theoretically, the unification of existing methods under the LMO framework offers a lens to evaluate and enhance algorithms in the field of data-driven model architecture, guiding the development of future techniques.

Future Directions

This research opens several avenues for future exploration. Significantly, the LMO framework can act as a baseline for developing more sophisticated methods that manage the trade-offs between computational constraints and modeling efficacy. Furthermore, Aioli and similar techniques stand to benefit from adaptations that consider training resource dynamics more granularly, potentially broadening their applicability to larger models and datasets.

In summary, this paper not only critiques existing data mixing methodologies but also significantly redefines them along a more unified framework, with Aioli paving the way for advancements in how LMs are trained across complex data landscapes.