GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization 事件

GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization arXiv:2605.27934v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves language model reasoning, but its reliance on domain-specific verifiers, sparse outcome rewards, and coarse-grained credit assignment limits its applicability. We introduce GeneralThinker, an on-policy framework that reformulates reasoning supervision as dense answer-conditioned optimization, en