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Paper · Self-reward and self-play

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

A localized self-distillation recipe allows small open-weight models to improve their multi-step financial reasoning without relying on external frontier models.

How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning
Figure 1 of the paper, arXiv. Source
The loop

A student model identifies its own reasoning errors and writes critiques to condition a teacher version of itself. This teacher then distills the improved logic back into the student over a short rollout.

The loop
How Much Does the Hint Matter? Scope Conditions for Hindsight Self-Distillation in Financial Reasoning
Paper · arXiv
  1. Student model fails a reasoning step
  2. Student writes a critique of the error
  3. Critique conditions a frozen teacher model
  4. Teacher distills improved logic back into student
  1. Student model fails a reasoning step
  2. Student writes a critique of the error
  3. Critique conditions a frozen teacher model
  4. Teacher distills improved logic back into student
↻ The improved system does the next round, and the loop turns again.

Why it is a road to recursion

It demonstrates that small models can correct their own reasoning trajectories without relying on external frontier models, enabling localized self-improvement.

Evidence

Applied to GPT-OSS-20B, the method successfully distills hint-conditioned corrections back into the student over short rollouts under reverse KL.

self-distillationfinancial-reasoninghindsight

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