Learning to Reason from Reference Samples Alone: Reinforced Adversarial Learning for Modern LLM Post-Training
arXiv:2610.38851v1 Announce Type: new 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…