Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
Quick summary
arXiv:2608.20169v2 Announce Type: replace-cross Abstract: We present a novel approach to efficient LLM harness optimization through adaptive validation task selection. Harness optimization iteratively rewrites the harness code based on validation performance, enabling substantial performance gains without updating the underlying model weights. Existing approaches, however, evaluate a fixed validation set in full at every iteration, incurring substantial evaluation costs even on tasks that become less discriminative as the harness evolves. We propose $\textbf{Task-CoEvolve}$, which co-evolves t
Key takeaways
- arXiv:2608.20169v2 Announce Type: replace-cross Abstract: We present a novel approach to efficient LLM harness optimization through adaptive validation task selection.
- Harness optimization iteratively rewrites the harness code based on validation performance, enabling substantial performance gains without updating the underlying model weights.
- Existing approaches, however, evaluate a fixed validation set in full at every iteration, incurring substantial evaluation costs even on tasks that become less discriminative as the harness evolves.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
Member comments