Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization
arXiv:2602.17066v1 Announce Type: new 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 statis
arXiv:2602.17066v1 Announce Type: new 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 computational overhead.
Executive Summary
The article introduces Predictive Batch Scheduling (PBS), a novel training optimization technique for accelerating language model convergence. PBS prioritizes high-loss samples during batch construction using a lightweight linear predictor trained online to estimate sample difficulty. The technique achieves 6-13% faster convergence with negligible computational overhead, demonstrating the effectiveness of token frequency statistics in encoding sample difficulty information.
Key Points
- ▸ Introduction of Predictive Batch Scheduling (PBS) for accelerating language model training
- ▸ Use of a lightweight linear predictor to estimate sample difficulty from static token-level features
- ▸ Achievement of 6-13% faster convergence with negligible computational overhead
Merits
Efficient Sample Prioritization
PBS enables efficient sample prioritization without requiring predefined difficulty metrics or expensive per-sample loss tracking.
Negligible Computational Overhead
The technique achieves faster convergence with minimal computational overhead, making it a practical solution for large-scale language model training.
Demerits
Limited Feature Set
The predictor relies on a limited set of four simple features, which may not capture the full complexity of sample difficulty.
Correlation Improvement Over Time
The predictor's correlation with actual loss improves over time, but the initial correlation of 0.14 may not be sufficient for effective sample prioritization.
Expert Commentary
The introduction of Predictive Batch Scheduling (PBS) marks a significant advancement in language model training optimization. By leveraging a lightweight linear predictor to estimate sample difficulty, PBS enables efficient sample prioritization without incurring substantial computational overhead. The technique's ability to achieve 6-13% faster convergence demonstrates its potential for accelerating large-scale language model training. However, further research is necessary to explore the limitations of the predictor's feature set and to investigate the applicability of PBS to diverse language model architectures and training scenarios.
Recommendations
- ✓ Further investigation into the expansion of the predictor's feature set to capture more complex sample difficulty metrics
- ✓ Exploration of PBS applications in diverse language model training scenarios, including multilingual models and transfer learning
