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.
Fin-SD improves local multi-step financial reasoning by using self-generated error critiques and truncated correct prefixes for on-policy distillation without external teachers.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning was first seen on Hugging Face · Daily papers on Oct 29, 2026. Since then it has appeared in 7 checks on 2 different days. Its best position was #27 of 30, reached on Oct 30, 01:28 UTC. It dropped off the list after Oct 30, 07:28 UTC.
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#27 on Hugging Face · Daily papers, on Oct 30, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 29, 2026 and has been on the list on 2 days (7 checks) since.