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• ASK (Adaptive Safety through Knowledge) combines smaller language models with trained RL policies to enhance out-of-distribution generalization without retraining.method 95%
• ASK employs Monte Carlo Dropout to assess uncertainty and queries the language model for action suggestions only when uncertainty exceeds a set threshold.method 90%
• In experiments on the FrozenLake environment, ASK shows no improvement in-domain.result 85%
• ASK demonstrates robust navigation in transfer tasks, achieving a reward of 0.95.result 90%
• Larger language models incur high computational costs, hindering real-time use.limitation 85%
• Effective neuro-symbolic integration requires careful orchestration rather than simple combination.technical 80%
• Successful out-of-distribution generalization requires sufficient model scale and effective hybridization mechanisms.technical 80%
• The selective use of language model queries preserves the efficiency of existing policies while leveraging language model reasoning in uncertain situations.method 85%

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