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English(EN) Gradient-Guided Reward Optimization for Inference-time Alignment

新方法增强LLM推理时的对齐

作者 PulseAugur 编辑部 · [5 个来源] ·

研究人员开发了在推理过程中改进大型语言模型对齐的新方法。一种名为BlendIn的方法使用概率模型混合来整合来自多个模型的知识，通过质量感知加权稳定对齐并弱化不可靠的指导。另一种方法，梯度引导奖励优化（GGRO），利用梯度信号在高度不确定区域注入提示令牌，从而引导生成而非仅仅重新排序。第三种观点将奖励模型优化视为Stackelberg博弈，提出奖励塑造来近似最优模型并提高用户效用，同时减轻奖励操纵。 AI

影响 这些推理时对齐技术可能带来更可靠、更鲁棒的LLM输出，尤其是在分布漂移的情况下，并且计算开销极小。

排序理由 多篇研究论文在arXiv上发表，介绍了LLM推理时对齐的新颖方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →

新方法增强LLM推理时的对齐

报道来源 [5]

  1. arXiv cs.AI TIER_1 English(EN) · Jin Gan, Xin Li, Jun Luo ·

    To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending

    arXiv:2606.11201v1 Announce Type: cross Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among different methods, inference-time alignment is often cheaper as it intervenes (…

  2. arXiv cs.LG TIER_1 English(EN) · Hankun Lin, Ruqi Zhang ·

    Gradient-Guided Reward Optimization for Inference-time Alignment

    arXiv:2606.09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of-$N$ and rejection sampling are widely used, they frame th…

  3. arXiv cs.AI TIER_1 English(EN) · Haichuan Wang, Tao Lin, Lingkai Kong, Ce Li, Hezi Jiang, Milind Tambe ·

    面向（推理时）对齐的奖励塑造：一种Stackelberg博弈视角

    arXiv:2602.02572v2 Announce Type: replace-cross Abstract: Existing alignment methods directly use the reward model learned from user preference data to optimize an LLM policy, subject to KL regularization with respect to the base policy. This practice is suboptimal for maximizing…

  4. arXiv cs.CL TIER_1 English(EN) · Ruqi Zhang ·

    梯度引导奖励优化用于推理时对齐

    Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation. While inference-time alignment methods such as Best-of-$N$ and rejection sampling are widely used, they frame the task as a sampling-intensive, reward-guided sear…

  5. Mastodon — mastodon.social TIER_1 English(EN) · AIsynestesia ·

    🤖 Guided Model Alignment Frameworks Gain Traction in AI Research Researchers are increasingly focusing on inference time alignment methods to improve the perfor

    🤖 Guided Model Alignment Frameworks Gain Traction in AI Research Researchers are increasingly focusing on inference time alignment methods to improve the performance of large language models. This shift in focus is driven by the need for more efficient and effective ways to align…

    链接 synestesia.uk/…/guided-model-alignment-fr… synestesia.uk/…/guided-mo

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