Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.

False information on the Internet has caused severe damage to society. Researchers have proposed methods to determine the credibility of news and have obtained good results. As different media sources (publishers) have different content generators (writers) and may focus on different topics or aspects, the word/topic distribution for each media source is divergent from others. We expose a challenge in the generalizability of existing content-based methods to perform consistently when applied to news from media sources non-existing in the training set, namely the cross-source failure. A cross-source setting can cause a decrease beyond in accuracy for current methods; content-sensitive features are considered one of the major causes of cross-source failure for a content-based approach. To overcome this challenge, we propose a syntactic network for news credibility (SYNC), which focuses on function words and syntactic structure to learn generalizable representations for news credibility and further reinforce the cross-source robustness for different media. Experiments with cross-validation on 194 real-world media sources showed that the proposed method could learn the generalizable features and outperformed the state-of-the-art methods on unseen media sources. Extensive analysis on the embedding feature representation represents a strength of the proposed method compared to current content embedding feature approaches. We envision that the proposed method is more robust for real-life application with SYNC on account of its good generalizability.