Based on official documentation and experimental data, HRM training requires special attention to the following points:
Data preparation
- Maintain sample diversity (e.g. Sudoku training using data augmentation techniques)
- It is sufficient to control the sample size around 1000 (too large may trigger overfitting)
Training Strategies
- Learning rate setting: recommended initial value of 7e-5 (single GPU) or 1e-4 (multi-GPU)
- Early stopping mechanism: stopping should be considered when validation accuracy reaches 98%
- Batch size control: 384 recommended for single GPU (e.g. RTX 4070)
Issue avoidance
- Numerical instability: add gradient clipping (threshold set to 1.0)
- overfitting: Use of weight decay (recommended value 1.0)
- <b]Convergence difficulties: Check if the FlashAttention installation version matches the GPU architecture
Typical training performance: It takes about 10 hours to train a difficult Sudoku model on an RTX 4070, which can be reduced to 10 minutes in an 8-card environment. Accuracy fluctuations typically ranged from ±2%.
This answer comes from the articleHRM: Hierarchical Reasoning Model for Complex ReasoningThe
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