Author

Beidou Wang

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#machine learning May 2026

PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents

PACEvolve++ is introduced, an advisor-model reinforcement learning framework for test-time policy adaptation in evolutionary search agents, which outperforms the state-of-the-art evolutionary search framework with frontier models, achieving faster convergence and stabilizing test-time training during evolutionary searc...

Minghao Yan, Bo Peng, Benjamin Coleman et al. · arXiv.org · 4 citations

#machine learning Jan 2026

PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution

Progress-Aware Consistent Evolution (PACEvolve), a systematic framework for governing agent memory and search dynamics, maintains a global view of search momentum and achieves state-of-the-art results across complex evolutionary benchmarks.

Minghao Yan, Bo Peng, Benjamin Coleman et al. · arXiv.org · 22 citations