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arXiv AI By Johnny Yu, Cyril Gorlla

How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning

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The paper introduces Fin‑SD, a localized hindsight self‑distillation method for multi‑step financial reasoning that uses a student’s own failed trajectory to generate a short critique of the first incorrect step. By truncating the trajectory before the error, conditioning a frozen copy of the student on the correct prefix plus the critique, and distilling this hint‑conditioned teacher back into the student, the approach eliminates the need for an external teacher or full reference solution. Experiments on GPT‑OSS‑20B show that Fin‑SD achieves a 3.03‑percentage‑point accuracy gain on the FinanceReasoning Hard subset, matches full‑reference on‑policy self‑distillation while using 9.5 % fewer tokens, and does not degrade performance on MMLU‑Pro or a fourteen‑metric compliance audit.

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