The World as a Curator: Learning from Market Outcomes for Financial Reasoning

Supervised fine tuning is widely used to adapt large language models to domain tasks, traditionally relying on high quality examples annotated by experts. Limited coverage of existing datasets and costly expert annotation make expanding high quality domain supervision a persistent challenge. Synthetic instruction and response pairs offer a scalable alternative, but internal judgments cannot independently verify agreement with actual outcomes when generation and selection share model priors without external evidence, while external feedback is often delayed, noisy, and confounded. To this end, we propose World Informed Selection with Energy and Reliability (WISER), which selects responses for student fine tuning using historical market feedback and its reliability to improve future financial event reasoning. Specifically, we lock candidates before outcome access, train a World Feedback Energy model using nonoverlapping selection and confirmation windows to learn nonadditive interactions among candidate attributes, market compatibility, and feedback reliability, and select its configuration through constrained Bayesian optimization within 64 evaluations. Extensive results on 255 post freeze events across China, the United States, Europe, and Japan show gains of 0.024 in Entity NDCG@5, 0.027 in Direction MCC, and 0.10 in cost adjusted Sharpe over Direct WF Score, demonstrating that selected supervision improves future reasoning and investment performance using event time inputs alone.