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Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data
Not provided in the abstract
self-distillationreasoningLLMverification
2610.18878
Builder Relevance
2h ago80%
Abstract
This paper presents a self-distillation technique for post-training reasoning LLMs that utilizes answer-only data to improve efficiency and effectiveness.
Reality Card
Core Claim
The proposed inverse reasoning model significantly improves performance on reasoning tasks, doubling pass@1 on hard prompts and outperforming rejection-sampling self-distillation metrics.
Method / Result
A single offline distillation round from the inverse model roughly doubles pass@1 on hard prompts.
Limitations
The method relies on the quality of the inverse reasoning model and may not generalize across all tasks.
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