We thank all reviewers for their valuable feedback and thoughtfull suggestions. To the best of our knowledge, there is no official implementation for the paper by Gu et al. (no link to the code However, in Section 5.1 we compare the lower bound on the objective we use with the one of Gu et These works do not report significant improvements in BLEU scores against the autoregressive baselines. Stern et al.(2019) focus on parallel decoding (with the final result matching the vanilla Transformer). NMT models for high-resource language pairs), we will add them should the paper get accepted. Note that we consider not only natural language output, but also Image-to-Latex, where output is LaTex formulas.

Neural circuits are typically comprised of populations of primary (excitatory) neurons and local (inhibitory) interneurons.

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Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and If you ran experiments... (a) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? Please refer to both main text and appendix for experiment details. Did you report error bars (e.g., with respect to the random seed after running experiments multiple All adaptation experiments in Procgen and RLBench are run for 3 seeds. Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal As stated in section 2, we use RTX A5000 GPUs each with 24GB memory. C2F-ARM algorithm and training framework are built based on the original author's implementation Did you mention the license of the assets?

Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve IG performance remains uncharted. We address this issue using a token-based IG framework, which relies on effective tokenizers to map images into token sequences. Currently, pixel reconstruction (e.g., VQGAN) dominates the training objective for tokenizers. In contrast, our approach adopts the feature reconstruction objective, where tokenizers are trained by distilling knowledge from pretrained IU encoders. Comprehensive comparisons indicate that tokeniz-ers with strong IU capabilities achieve superior IG performance across a variety of metrics, datasets, tasks, and proposal networks.

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.

Adap-tivity to the problem's structure is essential in order to obtain optimal regret upper