We want to thank the reviewers for their helpful comments. The dataset will be made available to any interested researchers. We agree with R3 that there are a lot of non-trivial modeling choices in our architecture. We call the first one unit-based and the latter token-based. We apologize for writing some of the claims without referring to the evidence, like "orders from the last movement Our input representation is a result of both empirical findings and domain knowledge.

As overparameterized models become increasingly prevalent, training loss alone offers limited insight into generalization performance. While smoothness has been linked to improved generalization across various settings, directly enforcing smoothness in neural networks remains challenging. To address this, we introduce Distributional Input Projection Networks (DIPNet), a novel framework that projects inputs into learnable distributions at each layer. This distributional representation induces a smoother loss landscape with respect to the input, promoting better generalization. We provide theoretical analysis showing that DIPNet reduces both local smoothness measures and the Lipschitz constant of the network, contributing to improved generalization performance. Empirically, we validate DIPNet across a wide range of architectures and tasks, including Vision Transformers (ViTs), Large Language Models (LLMs), ResNet and MLPs. Our method consistently enhances test performance under standard settings, adversarial attacks, out-of-distribution inputs, and reasoning benchmarks. We demonstrate that the proposed input projection strategy can be seamlessly integrated into existing models, providing a general and effective approach for boosting generalization performance in modern deep learning.

Weaknesses: I'm concerned that the comparison to DipNet, the prior state of the art, is misleading because the authors initialize their algorithm by effectively computing a best response to DipNet. Since they beat DipNet, the authors say that they are "stronger" than DipNet. However, beating DipNet is expected if one were to compute a best response to DipNet, even if the best response is a "weaker" policy. To illustrate why this is a problem, one could imagine a situation like Rock-Paper-Scissors where DipNet is biased toward playing Rock, so the techniques introduced in this paper effectively learn to always choose Paper. Paper beats Rock, but one is not "stronger" than the other.

The dynamically changing alliances mean that the domain of diplomacy presents unique challenges for agents. I agree with the authors that this means that diplomacy is'deserving of special attention', I would consider the full game to be a grand challenge for multi-agent research. With recent progress in large-scale RL focusing on single-agent and 2-player zero sum games, this problem is particularly timely. This work presents state of the art agents trained with deep learning. To my knowledge this is the first successful application of deep learning to diplomacy.

Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly focused on reducing the number of parameters and floating-point operations through various network designs. Although these methods can decrease the number of parameters and floating-point operations, they may not necessarily reduce actual running time. To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance. Specifically, we leverage enhanced high-resolution output as additional supervision to improve the learning ability of lightweight student networks. Upon convergence of the student network, we further simplify our network structure to a more lightweight level using reparameterization techniques and iterative network pruning. Meanwhile, we adopt an effective lightweight network training strategy that combines multi-anchor distillation and progressive learning, enabling the lightweight network to achieve outstanding performance. Ultimately, our proposed method achieves the fastest inference time among all participants in the NTIRE 2023 efficient super-resolution challenge while maintaining competitive super-resolution performance. Additionally, extensive experiments are conducted to demonstrate the effectiveness of the proposed components. The results show that our approach achieves comparable performance in representative dataset DIV2K, both qualitatively and quantitatively, with faster inference and fewer number of network parameters.