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PREFINE method enhances AI safety alignment using preference tuning

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed PREFINE, a novel method for adapting pre-trained reinforcement learning policies to incorporate safety constraints without full retraining. This technique leverages trajectory-level preferences, similar to how Direct Preference Optimization (DPO) is used for LLMs, to fine-tune policies for safer behavior. PREFINE has demonstrated a significant reduction in constraint violations and failures, exceeding 60%, while preserving original reward performance. The method offers improved data and computational efficiency compared to traditional offline RL or imitation learning approaches. AI

影响 Enhances AI safety by enabling cost-aware behavior adaptation in pre-trained models, improving efficiency and reducing failures.

排序理由 The cluster contains an academic paper detailing a new method for AI safety alignment.

在 arXiv cs.AI 阅读 →

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Richa Verma, Bavish Kulur, Sanjay Chawla, Balaraman Ravindran ·

    PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment

    arXiv:2605.21225v1 Announce Type: cross Abstract: We address the problem of making a pre-trained reinforcement learning (RL) policy safety-aware by incorporating cost constraints without retraining it from scratch. While costs could be numerically encoded, we assume a more genera…

  2. arXiv cs.AI TIER_1 English(EN) · Balaraman Ravindran ·

    PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment

    We address the problem of making a pre-trained reinforcement learning (RL) policy safety-aware by incorporating cost constraints without retraining it from scratch. While costs could be numerically encoded, we assume a more general setting is when costs are provided as preference…

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