PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
PACEvolve++ introduces an advisor-model reinforcement learning framework to adapt evolutionary search agents during testing. It decouples strategic decision-making (the advisor) from implementation (the frontier model), allowing for faster convergence and more stable training. This phase-adaptive approach optimizes the agent's strategy as the evolutionary process moves from broad exploration to precise refinement, leading to superior performance across diverse engineering tasks like load balancing and protein fitness extrapolation.