Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data
arXiv:2610.18878v1 Announce Type: new Abstract: Post-training reasoning LLMs often requires making a tradeoff between costly supervision and compute inefficiency: supervised finetuning typically requires reasoning traces, which is data inefficient since in many applications one only has prompt-answ…
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- 2026-10-15 04:00 · arXiv cs.LG
Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data