NVIDIA Introduces CLIMB: A Framework for Iterative Data Mixture Optimization in Language Model Pretraining

Challenges in building effective pre -training data mixtures

Since the large linguistic models (LLMS) in size and ability, the selection of pre -training data remains a decisive specific performance. Most LLMS are trained in large web data sets such as common encroachment, which provide wide coverage but lack explicit field stickers. This provides difficulties in coordinating the mixtures that balance between general knowledge with the experience of the field.

The activation of the manual data group, as shown in efforts such as lint, is intense and does not expand well. Moreover, the non -linear relationship between data formation and models’ performance makes without the distinction of determining the optimal field data. These restrictions stimulate the need for methods of selecting automatic, developmental and adaptive data.

Climbing: a repetitive framework to discover the data mixture

To address this, NVIDIA researchers suggest hiking–Bootstrapping the group -based repetitive data mixture– Working framework automation to discover and refine data mixtures of the language model before training. Climb combines non -overseeing assembly with a repeated improvement to determine the well -appropriate mixtures of general or domain goals.

The pipeline begins with the inclusion of large -scale text data in a semantic space using Pretraines. The K-Means group is then applied to organize data in knit groups, which are trimmed and merged based on the quality and repetition of the content. This is the basis for building the candidate mixtures.

Next, Climb uses an agent for assessing the mixtures from which samples were taken and suitable for slope -based prediction (for example, Lightgbm) to estimate the performance of the mixture. Repeated boot procedure gradually improves sampling space, which gives high performance configurations. This allows climbing to manipulate an effective data mixture under a fixed account budget.

Technical details and design considerations

The improvement process is framed as a two -level problem: at the lowest level, the agent models are trained in the compliance of the candidates; On the upper floor, the indicator is learned to bring the results of the performance. This prediction is guided by more samples and pruning, allowing effective exploration of the mixture of the mixture.

Climb supports folds in the weight of the mixture, encouraging the discovery of integrated data groups related to the field. The use of assembly on implications-instead of features at the level of the distinctive symbol-affects semantic cohesion within groups. Repeated revision was organized to achieve balance in the breadth of (coverage of the search area) deeply (predictive accuracy), and detection studies confirm that allocating accurate account through repetitions improves rapprochement and final performance.

The frame also shows durability through the sizes of the agent models and cluster fluctuations. While the larger agent models resulted in a little better predictions, even smaller models maintain the main structural trends. Likewise, climbing is relatively sensitive to the number of the first mass, provided that it is within a reasonable scope.

Evaluation and experimental notes

Climb has been evaluated in many general thinking tasks, including PIQA, Arc (Easy and Challenge), Hellaswag and Winogrande. The 1B model has achieved a medium -sized climbing discovery mixtures 60.41 %It surpasses the comparative foundation lines such as Doremi and RGMIX.

When it spanned 400B-TOKEN PRTRING, this 1B model outperformed Llama-3.2B by 2.0 % over a wide range of criteria. Likewise, in the SUB-500M model category, climbing training has resulted in fixed improvements on models like Smollm and Tinyllama.

The field specializes highlights the benefit of Climb. In the target MMLU standards via STEM, Humanities, and Social Sciences, trained models surpassed both random choices and comprehensive research dangers. The repetitive process showed fixed gains at each stage, indicating effective guidelines of the alert model.

To facilitate cloning and further research, NVIDIA released:

• hiking: A group of 1.2 trillion Tonkin organized in 20 semantic groups.
• Climbmix: A good combination of 400 billion improved for effective training.

Climbmix models surpass this trainer on data collections such as Nemotron-CC and SMOLLM under equivalent symbolic budgets, indicating improving scaling properties.

conclusion

Climb provides a methodological method to improve data mixtures in Pretering LLM. By combining semantic assembly with a repetitive research based on the agent, it avoids relying on manual illustrations or fixed inference. The method supports both general and specialized training goals and adaptation to changing account restrictions and data.

This framework contributes to the ongoing efforts in the artificial intelligence centered on data by providing a capable and initial alternative to handmade data tubes. Its experimental performance emphasizes the importance of improving the data mixture in maximizing the benefit of the model, especially in light of the fixed resource budgets.

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