Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data.

arXiv:2610.18878v1 Announce Type: cross 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 m