Generic Feedback Matches Detailed Critiques in Fin-SD Study
The Fin-SD researchers report applying hindsight self-distillation to GPT-OSS-20B for multi-step financial reasoning, where a student locates its first incorrect step and generates a critique before a frozen copy distills the result back under reverse KL. The author-linked Reddit summary says a fixed generic feedback line matched the roughly three-point gain from detailed self-written critiques, while critiques from a model six times larger did not materially improve performance; Fin-SD also matched OPSD trained with full reference solutions while using 9.5% fewer tokens.
The reported results suggest that positional information from hindsight distillation may provide much of the training benefit without requiring detailed critiques or feedback from a substantially larger model.
We study hindsight self-distillation for multi-step financial reasoning on GPT-OSS-20B. The student finds its own first wrong step and writes a critique. A frozen copy, conditioned on the prefix plus the critique, is distilled back into the student under reverse KL. Notably, replacing the critique with a fixed generic feedback line matches the full gain of about 3 points, and critiques from a 6x larger model do not materially improve performance, suggesting that the positional information provided by hindsight distillation significantly decreases the privileged information needed for effective training. Fin-SD matches OPSD from full reference solutions with 9.5% fewer tokens.
Paper: https://arxiv.org/abs/2610.36082 | Code: https://github.com/johnnyhyu/fin-sd
We'd love to find the limits of this approach. Let us know your thoughts!
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