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This work analyzes the decoding trajectories of LLaDA 2.0 and identifies a recurring diffusion confidence trap, which improves LLaDA 2.0 over confidence-based decoding, leading to more reliable mathematical reasoning.
This paper first proves that TFS is a sufficient condition for weight disentanglement, and finds that TFS also gives rise to an observable geometric consequence: weight vector orthogonality, which positions TFS as the common cause for both the desired functional outcome and a measurable geometric property.
This work introduces an evolutionary, task-agnostic, strategy-guided, executably-checkable data synthesis framework that, from minimal seed supervision, jointly synthesizes problems, diverse candidate solutions, and verification artifacts, and iteratively discovers strategies via a consistency-based evaluator that enforces agreement be-tween human-annotated and strategy-induced checks.
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