CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training

Introduction to CLIMB

The paper “CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training” introduces an automated framework to optimize the mixture of diverse, unlabeled pre-training datasets for large language models by iteratively clustering data in a semantic space using the model’s own embeddings, evaluating model performance on these clusters, and dynamically adjusting sampling weights to prioritize more informative or challenging data. This bootstrapping approach enables the model to self-refine its training data distribution without manual intervention or explicit domain labels, leading to improved downstream task performance; for example, a 1-billion-parameter model trained on a CLIMB-optimized 400-billion-token mixture outperforms the state-of-the-art Llama-3.2-1B by 2%, and domain-specific optimization yields up to 5% gains. The paper also releases large-scale clustered datasets (ClimbLab and ClimbMix) to facilitate further research, demonstrating that dynamic, data-driven mixture adjustment is a promising direction for efficient and effective language model pre-training.

Problem Addressed

• Pre-training large language models (LLMs) requires massive, diverse datasets from multiple sources (e.g., web text, code, books).
• The mixture proportions of these data sources critically affect downstream model performance.
• Manually tuning these mixtures is costly and heuristic-driven, requiring multiple full pre-training runs.
• The goal is to automate and dynamically optimize the data mixture during pre-training to improve efficiency and effectiveness.

Core Idea of CLIMB

• CLIMB introduces an iterative bootstrapping framework that uses the model’s own learning signals to guide data mixture refinement.
• It clusters training data based on semantic embeddings produced by the current model state.
• Model performance on these clusters (e.g., loss) informs how to adjust sampling weights of original data sources for subsequent training.

Methodology: How CLIMB Works

• Step 1: Embedding & Clustering
  Sample a subset of data, embed using the current LLM state, and cluster (e.g., K-means) to group semantically similar data.
• Step 2: Performance Evaluation per Cluster
  Measure model loss on each cluster; high loss indicates challenging/informative data, low loss indicates mastered data.
• Step 3: Data Mixture Refinement
  Map clusters back to original data sources and adjust sampling weights: upweight sources linked to high-loss clusters and optionally downweight low-loss clusters.
• Step 4: Bootstrapping
  Continue pre-training with updated data mixture; repeat the cycle iteratively to continuously improve data selection.
• Uses a smaller proxy model and predictor to efficiently evaluate mixtures, reducing computational overhead^[2].

Key Concepts

• Bootstrapping: Model self-improves by using its current understanding to refine future training data.
• Clustering: Groups data semantically based on model embeddings to identify meaningful data types.
• Data Mixture Optimization: Dynamically finds optimal proportions of diverse data sources for pre-training.

Experiments and Results

• CLIMB outperforms fixed or heuristic data mixtures on various downstream benchmarks (e.g., SuperGLUE, commonsense reasoning, QA).
• Demonstrated on models trained up to 400B tokens, with a 1B parameter model surpassing Llama-3.2-1B by 2%.
• Domain-specific optimization (e.g., Social Sciences) yields up to 5% improvement over random sampling.
• The optimized mixtures are often non-intuitive, highlighting the value of data-driven dynamic adjustment^[2][3].

Contributions and Significance

• Automates data mixture tuning, reducing reliance on expensive manual trials or heuristics.
• Leverages model-internal signals to create a self-refining data selection loop.
• Shows that dynamic data mixture adjustment improves final LLM performance and domain specialization.
• Introduces new datasets (ClimbLab: 1.2T tokens clustered corpus; ClimbMix: 400B token optimized subset) for research and efficient pre-training^[2].

Limitations and Considerations

• Additional computational cost due to clustering and evaluation steps, though mitigated by proxy models.
• Introduces new hyperparameters (clustering parameters, update frequency, weighting strategy).
• Effectiveness depends on quality of model embeddings, especially early in training.