Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
[Przetwarzanie języka] Autorzy: Atsuyuki Miyai, Kiyoharu Aizawa, Toshihiko Yamasaki
We present a novel approach to efficient LLM agent 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. Task-CoEvolve builds on the observation that tasks on which candidate harnesses disagree are more informative for distinguishing among them than tasks that are consistently solved or failed. It …
- Kategoria arxiv
- cs.CL
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- Przetwarzanie języka