Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
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 the validation tasks with the harness by addressing two challenges: selecting informative tasks and estimating full-set performance from partial evaluations...
| Authors | Atsuyuki Miyai, Kiyoharu Aizawa, Toshihiko Yamasaki |
|---|---|
| Primary category | cs.CL |
| Published | 20 August 2026 |
| Last updated at source | 24 August 2026 |
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