A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs (2410.18779v1)

Abstract: A primary challenge in LLM development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such as next-token prediction) over a large corpus. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by suitably leveraging a small LLM (SLM). In particular, this paradigm relies on an SLM to both (1) provide soft labels as additional training supervision, and (2) select a small subset of valuable ("informative" and "hard") training examples. Put together, this enables an effective transfer of the SLM's predictive distribution to the LLM, while prioritizing specific regions of the training data distribution. Empirically, this leads to reduced LLM training time compared to standard training, while improving the overall quality. Theoretically, we develop a statistical framework to systematically study the utility of SLMs in enabling efficient training of high-quality LLMs. In particular, our framework characterizes how the SLM's seemingly low-quality supervision can enhance the training of a much more capable LLM. Furthermore, it also highlights the need for an adaptive utilization of such supervision, by striking a balance between the bias and variance introduced by the SLM-provided soft labels. We corroborate our theoretical framework by improving the pre-training of an LLM with 2.8B parameters by utilizing a smaller LM with 1.5B parameters on the Pile dataset.

An Overview of "A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs"

The paper addresses the computational challenges faced in pre-training LLMs by introducing an approach that leverages small LLMs (SLMs) to enhance the efficiency and quality of the training process. This paper proposes a methodology wherein SLMs are utilized to provide soft labels and select informative training examples, ultimately facilitating a more effective transfer of information to LLMs.

The researchers present both empirical and theoretical frameworks to validate this paradigm. Empirically, the approach demonstrates a reduction in training time coupled with improvements in model quality. The SLM-assisted training method is tested with a 2.8B parameter LLM utilizing a 1.5B parameter SLM on the Pile dataset, yielding superior results compared to conventional training methods.

Methodology

1. Soft Labels and Data Selection: SLMs are employed to generate soft labels, offering supplementary supervision during training. Additionally, these models assist in identifying valuable subsets of training data that are both informative and challenging.
2. Statistical Framework: The theoretical model delineates how SLM-generated supervision, though potentially low in quality, can be advantageous by properly balancing bias and variance in training.
3. Adaptive Utilization: The importance of adaptively utilizing SLM-derived supervision is emphasized, suggesting that the focus should be on scenarios where SLM predictions align closely with the true data distribution.
4. Knowledge Distillation (KD): Through KD, the paper extends the classic teacher-student model, showing improved training effectiveness even when using a weaker teacher model (SLM) to guide a stronger student (LLM).

Key Findings

• The empirical results show that the proposed method reduces LLM training time while enhancing performance metrics, such as few-shot learning accuracy.
• The adaptation at training allows SLMs to prioritize easier examples initially, letting LLMs refine their focus during subsequent training phases.
• Utilizing SLMs during the early stages of LLM pre-training captures simpler patterns, reducing overall computational demands.

Implications

The research provides a pragmatic approach to LLM training, suggesting a pathway to achieving efficient computation without sacrificing model quality. The implications are particularly meaningful given the substantial resources typically required for LLM development.

Future Directions: The potential for SLMs to be used in this capacity sets a precedent for exploring further instantiations of such architectures; seeking novel architectures that could inherently combine the efficiencies of SLM with the capabilities of LLM is a promising avenue.

In summary, this paper effectively demonstrates how small models, traditionally overshadowed by their larger counterparts, can play a pivotal role in optimizing the creation and efficiency of LLMs. By leveraging concise, targeted supervision, the paper not only presents a technique that holds significant promise but also paves the way for further exploration into scalable AI development practices.