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This paper investigates why text-based optimization (TextGrad) fails for language agents, showing that while frozen agents can follow good policies, they cannot reliably learn and select policies from their own trajectories.
This paper identifies two failure modes in multi-objective prompt optimization for LLM judges using textual gradients: gradient dilution during optimization and instruction interference during inference, showing that joint gradient processing loses criterion-specific information.