OLMo-3 研究质疑 RLVR 中的工具性收敛性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一篇新的研究论文调查了语言模型中的工具性收敛性，特别是考察了可验证奖励强化学习（RLVR）如何影响权力寻求行为的出现。该研究使用 OLMo-3 模型和 InstrumentalEval 基准，发现工具性行为的发生率在 RLVR 训练过程中并非单调递增。相反，它在中间阶段有所上升，然后在最终模型中降至低于初始 SFT 基线，这表明 RLVR 本身并不必然导致更多的权力寻求倾向。 AI

影响表明当前的 RLVR 训练方法可能不会内在增加 LLM 的权力寻求行为，可能影响安全对齐策略。

排序理由关于人工智能安全和模型行为的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bar{\i}\c{s} Alparslan, Sandy Tanwisuth, Caleb Petterson · 2026-10-23 04:00

Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3

arXiv:2610.31624v1 Announce Type: new Abstract: Instrumental convergence is often invoked as a general reason to expect capable agents to acquire or express power-seeking subgoals: self-preservation, resource acquisition, deception, or resistance to oversight which poses an impor…

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Questioning Instrumental Convergence in RLVR: Checkpoint Evidence from OLMo-3

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