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Results show that post-training can substantially improve financial forecasting performance, together with changes in how the model investigates the market, on this outcome-selected benchmark.
AQuA, which comprises two separate language-model-driven research systems: one for symbolic factor discovery and one for trainable model development, is presented, which comprises two separate language-model-driven research systems that implement recursive self-improvement at the level of the research process.
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