The deployment of ever-larger machine learning models reflects a growing
consensus that the more expressive the model$\unicode{x2013}$and the more data
one has access to$\unicode{x2013}$the more one can improve performance. As
models get deployed in a variety of real world scenarios, they inevitably face
strategic environments. In this work, we consider the natural question of how
the interplay of models and strategic interactions affects scaling laws. We
find that strategic interactions can break the conventional view of scaling
laws$\unicode{x2013}$meaning that performance does not necessarily
monotonically improve as models get larger and/ or more expressive (even with
infinite data). We show the implications of this phenomenon in several contexts
including strategic regression, strategic classification, and multi-agent
reinforcement learning through examples of strategic environments in
which$\unicode{x2013}$by simply restricting the expressivity of one's model or
policy class$\unicode{x2013}$one can achieve strictly better equilibrium
outcomes. Motivated by these examples, we then propose a new paradigm for
model-selection in games wherein an agent seeks to choose amongst different
model classes to use as their action set in a game.

通过研究机器学习模型的扩展部署，本论文考察了模型和策略交互对规模定律的影响，发现战略交互可能破坏了常规的规模定律，同时提出了一种新的游戏模型选择范式。

重新思考战略环境下的学习尺度定律

Rethinking Scaling Laws for Learning in Strategic Environments

Algorithms for text-generation in dialogue can be misguided. For example, in
task-oriented settings, reinforcement learning that optimizes only task-success
can lead to abysmal lexical diversity. We hypothesize this is due to poor
theoretical understanding of the objectives in text-generation and their
relation to the learning process (i.e., model training). To this end, we
propose a new theoretical framework for learning to generate text in dialogue.
Compared to existing theories of learning, our framework allows for analysis of
the multi-faceted goals inherent to text-generation. We use our framework to
develop theoretical guarantees for learners that adapt to unseen data. As an
example, we apply our theory to study data-shift within a cooperative learning
algorithm proposed for the GuessWhat?! visual dialogue game. From this insight,
we propose a new algorithm, and empirically, we demonstrate our proposal
improves both task-success and human-likeness of the generated text. Finally,
we show statistics from our theory are empirically predictive of multiple
qualities of the generated dialogue, suggesting our theory is useful for
model-selection when human evaluations are not available.

本文提出了一种新的理论框架，旨在分析文本生成中多方面的目标并为适应未见数据的学习者提供理论保证，应用于 GuessWhat？！视觉对话游戏的数据转移研究，提出了一种新的算法。实验证明，本文提出的算法不仅改善了任务成功率，而且提高了生成文本的人类相似度，并且本文的统计数据能够预测对话的多个质量，是模型选择的一个有用指标。