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[2602.17066] Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization
Summary
The paper presents Predictive Batch Scheduling (PBS), a technique that accelerates language model training by prioritizing high-loss samples, achieving faster convergence with minimal computational overhead.
Why It Matters
As language models grow in complexity, optimizing training efficiency becomes critical. PBS offers a novel approach that enhances convergence speed without the need for extensive computational resources, making it relevant for researchers and practitioners in AI and machine learning.
Key Takeaways
- PBS dynamically prioritizes high-loss samples during training.
- Achieves 6-13% faster convergence compared to traditional methods.
- Utilizes a lightweight linear predictor based on simple token-level features.
- Correlation between predicted and actual loss improves significantly during training.
- Offers an efficient alternative to curriculum learning without heavy computational costs.
Computer Science > Artificial Intelligence arXiv:2602.17066 (cs) [Submitted on 19 Feb 2026] Title:Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization Authors:Sumedh Rasal View a PDF of the paper titled Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization, by Sumedh Rasal View PDF HTML (experimental) Abstract:We introduce Predictive Batch Scheduling (PBS), a novel training optimization technique that accelerates language model convergence by dynamically prioritizing high-loss samples during batch construction. Unlike curriculum learning approaches that require predefined difficulty metrics or hard example mining methods that demand expensive per-sample loss tracking, PBS employs a lightweight linear predictor trained online to estimate sample difficulty from static token-level features. Our predictor achieves 0.44 correlation with actual loss using only four simple features: token frequency, sequence length, vocabulary diversity, and rare token ratio. Experiments on a 130M parameter transformer demonstrate that PBS achieves 6-13\% faster convergence measured by evaluation loss across training checkpoints, with the predictor's correlation improving from 0.14 to 0.44 over 10,000 training steps. These results validate that token frequency statistics encode meaningful information about sample difficulty, enabling effective curriculum learning with negligible computationa...
