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Experiments show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines, achieving the improved SR and CR in most evaluated settings, and suggest that adapting the reusable procedure governing tool use is a key direction for building more robust LLM trading agents.
EFT enables LLMs to evolve solutions across diverse optimization tasks, achieving over 10% performance gains and state-of-the-art results in challenging mathematical problems.
ZPPO reveals that embedding teacher responses in prompts rather than gradients can dramatically boost the performance of small student models on challenging tasks.
TIDE uncovers hidden problems in user contexts, achieving substantial gains in task coverage and resolution that traditional methods miss.
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