Learning to Reason from Reference Samples Alone: Reinforced Adversarial Learning for Modern LLM Post-Training

arXiv:2610.38851v3 Announce Type: replace Abstract: In many settings where LLM post-training is desirable, high-quality answers are recognizable but difficult to score with explicit rewards. Reinforcement learning with verifiable rewards (RLVR) is limited to tasks where an explicit correctness signal is available. Prior adversarial approaches to training reasoning LLMs either complement verifiable rewards with co-trained critics or require reference reasoning traces. We ask whether a co-trained LLM judge can turn reference answers alone into the sole reward for on-policy post-training of reaso

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