arXiv: cs.AI preprintsInternational6 November 2026
More: Mode-Optimized Reasoning with Self-Adaptive Effort for Self-Evolving Agents
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arXiv:2610.33684v2 Announce Type: replace Abstract: Self-evolving training improves the performance of agents on difficult tasks through verifiable feedback, but it often applies costly deliberation uniformly across tasks. To address this problem, we propose Mode-Optimized Reasoning with Self-Adaptive Effort (MORE), a self-evolving training framework that adaptively selects both reasoning form and computational effort for each task. Specifically, MORE follows a two-stage training paradigm: (i) \emph{Stage 1} establishes a compact prior over direct answering, short and long chain-of-thought, an
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