KALAVAI: Predicting When Independent Specialist Fusion Works -- A Quantitative Model for Post-Hoc Cooperative LLM Training

1. 1Fine-tune copies of a shared checkpoint independently
2. 2Calculate divergence between specialist models
3. 3Estimate cooperative value using the gain equation: gain = 0.82 x divergence - 2.72
4. 4Commit compute resources based on predicted gain

ML researchers and practitioners benefit from KALAVAI as it enables them to estimate cooperative value before committing compute, and team members responsible for model training and deployment can use this insight to optimize their workflows

💡 The gain from fusing independent specialist models is predictable and dependent on the divergence between models

💡 Predict cooperative LLM training gains with KALAVAI: gain = 0.82 x divergence - 2.72