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cloud.google.com·6h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Conflicting Supervision Moves Commitment, Not Capability: A 12.29{\sigma} arrangement effect that is exactly zero under a convention-agnostic score
TL;DR
Training a model on data from two incompatible conventions reveals that the order of data affects the model's commitment to a convention, not its capability. The study shows a significant arrangement effect that is zero under a convention-agnostic score, highlighting the importance of data ordering in model training.
✦ Why It Matters
Consider how data arrangement and learning-rate schedules impact your model's training outcomes today.
Key Takeaways
- Experiment with different data arrangements to observe their impact on model commitment.
- Evaluate learning-rate schedules to optimize training outcomes.
- Understand that traditional benchmarks may overlook subtle model behaviors.
- Consider the implications of data ordering in multi-convention training scenarios.
How It Works
The study reveals that the learning-rate schedule acts as an averaging operator, influencing how the model interprets the order of data. By separating the effects of data arrangement and learning-rate scheduling, the authors show that the model's commitment to a specific convention is determined by the order in which data is presented, rather than its overall capability.