AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves sequential computation and repeated access to model weights, leading to high decoding latency. Recent pipelined self-speculative decoding methods reduce this delay by drafti...
Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existing Risk-Averse RLHF addresses this issue by optimizing Conditional Value-at-Risk (CVaR), but it trains policies for fixed risk levels and therefore cannot adjust the desi...
Adaptive Multilevel Twisted SMC is proposed, which learns the rare-event twist through a sequence of progressively rarer intermediate events, ultimately leading to a more accurate final twist for the target rare event.
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