Private NLL Evaluation
Internal evaluation methodology using Negative Log Likelihood (NLL) on private datasets for making scaling and architecture decisions during model development. Employed by microsoft in mai-thinking-1 development for systematic model progression.
Data Composition for MAI-Thinking-1
Microsoft's private NLL evaluation set comprised:
- 50% code
- 17.5% STEM
- 17.5% math
- 10% general knowledge
- 5% multilingual
Role in Scaling Decisions
Used to evaluate candidate architectures during the scaling-ladder process, providing consistent performance measurement across different model scales and configurations.
Technical Implementation
Negative Log Likelihood provides a fundamental loss measurement that enables:
- Objective comparison between model architectures
- Scaling law analysis and extrapolation
- Data-driven decisions on architecture promotion
- Consistent evaluation across training iterations
Strategic Advantage
Private evaluation sets enable companies to make scaling decisions based on proprietary benchmarks that may better reflect target use cases than public benchmarks, while maintaining evaluation consistency across development cycles.
See also
- mai-thinking-1
- scaling-ladder
- efficiency-gain-metric
- Negative-Log-Likelihood