arXiv cs.CL· Johnny Yu, Cyril Gorlla·· 13 小时前AI 评分26
Fin-SD：金融推理中事后自蒸馏的提示到底有多重要？
How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning
AI 导读
Fin-SD 是一种面向多步推理的本地化事后自蒸馏方法，由学生模型自行定位首个错误步骤并写出简短批评，无需外部教师模型提供梯度。在 GPT-OSS-20B 上，该方法于 FinanceReasoning Hard 子集相比基座模型提升 3.03 pp，但用通用反馈替换批评内容后增益相同，说明收益来自监督施加的位置而非提示内容。
正文 · 原文
Abstract:Financial institutions operating under data-residency constraints are restricted to open-weight models small enough to locally host, which trail frontier systems on multi-step quantitative reasoning. On-policy distillation narrows such gaps by supervising a student on its own rollouts, but traditional OPD requires a strong external teacher, and on-policy self-distillation (OPSD) conditions on a full reference solution. We ask how much of that signal is necessary. We present Fin-SD, a localized hindsight self-distillation recipe for multi-step reasoning. Given a problem the student fails, the student itself identifies the first incorrect reasoning step and writes a short critique of that step. We truncate the student's trajectory immediately before the error (aiming to obtain a correct prefix), condition a frozen copy of the student on the same prefix plus the hint, and distill that hint-conditioned teacher into the student over a short rollout under reverse KL. No external model contributes a gradient. We apply Fin-SD to GPT-OSS-20B on a custom multi-step financial reasoning benchmark that requires deriving formula inputs from realistic scenarios via accounting conventions. We find: (i) replacing the critique with a generic-feedback control matches Fin-SD's entire 3.03 pp accuracy gain over the base model on the FinanceReasoning Hard subset, suggesting the gain comes from where supervision is applied, not hint content; (ii) the model writing the hint does not need to be stronger than the model receiving it: hints from same-family GPT-OSS-120B, at roughly 6x the parameters, yield no materially greater benefits than self-written hints; and (iii) Fin-SD matches on-policy self-distillation from full reference solutions on accuracy while generating 9.5% fewer tokens. With Fin-SD, general reasoning on MMLU-Pro and a fourteen-metric compliance audit on FinTrust show no material regression.
| Comments: | 8 pages, 3 figures, 5 tables. Accepted at the 7th ACM International Conference on AI in Finance (ICAIF '26), Milan, Italy. Code and data: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.36082 [cs.LG] |
| (or arXiv:2610.36082v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36082 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Cyril Gorlla [view email]
[v1]
Mon, 26 Oct 2026 18:07:20 UTC (110 KB)
来源：arXiv cs.CL · arxiv.org