While Large Language Models (LLMs) have demonstrated their proficiency in
complex reasoning tasks, their performance in dynamic, interactive, and
competitive scenarios - such as business strategy and stock market analysis -
remains underexplored. To bridge this gap, we formally explore the dynamic
reasoning capabilities of LLMs for decision-making in rapidly evolving
environments. We introduce two game theory-based pilot challenges that mirror
the complexities of real-world dynamic decision-making. These challenges are
well-defined, enabling clear, controllable, and precise evaluation of LLMs'
dynamic reasoning abilities. Through extensive experiments, we find that
existing reasoning methods tend to falter in dynamic settings that require
k-level thinking - a key concept not tackled by previous works. To address
this, we propose a novel reasoning approach for LLMs, named "K-Level
Reasoning". This approach adopts the perspective of rivals to recursively
employ k-level thinking based on available historical information, which
significantly improves the prediction accuracy of rivals' subsequent moves and
informs more strategic decision-making. This research not only sets a robust
quantitative benchmark for the assessment of dynamic reasoning but also
markedly enhances the proficiency of LLMs in dynamic contexts.

通过提出一种名为 “K 级推理” 的新型推理方法，我们在大型语言模型中加强了动态情境中的决策制定能力，并为动态推理能力的评估设定了一个坚实的定量基准。

利用大型语言模型进行 K 级推理

K-Level Reasoning with Large Language Models

The standard problem setting in cooperative multi-agent settings is self-play
(SP), where the goal is to train a team of agents that works well together.
However, optimal SP policies commonly contain arbitrary conventions
("handshakes") and are not compatible with other, independently trained agents
or humans. This latter desiderata was recently formalized by Hu et al. 2020 as
the zero-shot coordination (ZSC) setting and partially addressed with their
Other-Play (OP) algorithm, which showed improved ZSC and human-AI performance
in the card game Hanabi. OP assumes access to the symmetries of the environment
and prevents agents from breaking these in a mutually incompatible way during
training. However, as the authors point out, discovering symmetries for a given
environment is a computationally hard problem. Instead, we show that through a
simple adaption of k-level reasoning (KLR) Costa Gomes et al. 2006,
synchronously training all levels, we can obtain competitive ZSC and ad-hoc
teamplay performance in Hanabi, including when paired with a human-like proxy
bot. We also introduce a new method, synchronous-k-level reasoning with a best
response (SyKLRBR), which further improves performance on our synchronous KLR
by co-training a best response.

通过同步训练所有层次的简单 k 级别推理适应，我们可以在 Hanabi 中获得具有竞争力的零射协调和特定的临时团队玩法表现，包括与类人代理机器人配对。