Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree
Search) can dramatically boost LLM performance on complex mathematical
reasoning tasks. However, they often require more than 10 times the
computational resources of greedy decoding due to wasteful search strategies,
making them difficult to be deployed in practical applications. This study
introduces a novel guided tree search algorithm with dynamic node selection and
node-level exploration budget (maximum number of children) calculation to
tackle this issue. By considering the search progress towards the final answer
(history) and the guidance from a value network (future) trained without any
step-wise annotations, our algorithm iteratively selects the most promising
tree node before expanding it within the boundaries of the allocated
computational budget. Experiments conducted on the GSM8K and TabMWP datasets
demonstrate that our approach not only offers competitive performance but also
enjoys significantly lower computational costs compared to baseline methods.

通过考虑搜索进展和价值网络的引导，本研究介绍了一种新颖的有引导的树搜索算法，用于在可接受的计算成本内提高性能。实验结果显示，该方法不仅具有竞争力的性能，而且相对于基准方法而言计算成本显著降低。

LiteSearch: 高效的 LLM 树搜索

LiteSearch: Efficacious Tree Search for LLM

Neural network supported tree-search has shown strong results in a variety of
perfect information multi-agent tasks. However, the performance of these
methods on partial information games has generally been below competing
approaches. Here we study the class of simultaneous-move games, which are a
subclass of partial information games which are most similar to perfect
information games: both agents know the game state with the exception of the
opponent's move, which is revealed only after each agent makes its own move.
Simultaneous move games include popular benchmarks such as Google Research
Football and Starcraft.
In this study we answer the question: can we take tree search algorithms
trained through self-play from perfect information settings and adapt them to
simultaneous move games without significant loss of performance? We answer this
question by deriving a practical method that attempts to approximate a coarse
correlated equilibrium as a subroutine within a tree search. Our algorithm
works on cooperative, competitive, and mixed tasks. Our results are better than
the current best MARL algorithms on a wide range of accepted baseline
environments.

通过将自我博弈的树搜索算法应用于同时移动的游戏中，以近似粗略相关均衡为子程序的实际方法，提高了在合作、竞争和混合任务上的性能表现。

通过均衡逼近实现对同时行动博弈的树搜索

Tree Search for Simultaneous Move Games via Equilibrium Approximation

Tree search algorithms, such as branch-and-bound, are the most widely used
tools for solving combinatorial and nonconvex problems. For example, they are
the foremost method for solving (mixed) integer programs and constraint
satisfaction problems. Tree search algorithms recursively partition the search
space to find an optimal solution. In order to keep the tree size small, it is
crucial to carefully decide, when expanding a tree node, which question
(typically variable) to branch on at that node in order to partition the
remaining space. Numerous partitioning techniques (e.g., variable selection)
have been proposed, but there is no theory describing which technique is
optimal. We show how to use machine learning to determine an optimal weighting
of any set of partitioning procedures for the instance distribution at hand
using samples from the distribution. We provide the first sample complexity
guarantees for tree search algorithm configuration. These guarantees bound the
number of samples sufficient to ensure that the empirical performance of an
algorithm over the samples nearly matches its expected performance on the
unknown instance distribution. This thorough theoretical investigation
naturally gives rise to our learning algorithm. Via experiments, we show that
learning an optimal weighting of partitioning procedures can dramatically
reduce tree size, and we prove that this reduction can even be exponential.
Through theory and experiments, we show that learning to branch is both
practical and hugely beneficial.

使用机器学习确定任何一组划分程序的最佳加权，并通过实验证明，学习如何分叉在实践中非常有益，可以显著减小树的规模。