While large language models (LLMs) are increasingly being used for program
synthesis, they lack the global view needed to develop useful abstractions;
they generally predict programs one at a time, often repeating the same
functionality. Generating redundant code from scratch is both inefficient and
error-prone. To address this, we propose Refactoring for Generalizable
Abstraction Learning (ReGAL), a gradient-free method for learning a library of
reusable functions via code refactorization, i.e. restructuring code without
changing its execution output. ReGAL learns from a small set of existing
programs, iteratively verifying and refining its abstractions via execution. We
find that the shared function libraries discovered by ReGAL make programs
easier to predict across diverse domains. On three datasets (LOGO graphics
generation, Date reasoning, and TextCraft, a Minecraft-based text game), both
open-source and proprietary LLMs improve in accuracy when predicting programs
with ReGAL functions. For CodeLlama-13B, ReGAL results in absolute accuracy
increases of 11.5% on graphics, 26.1% on date understanding, and 8.1% on
TextCraft, outperforming GPT-3.5 in two of three domains. Our analysis reveals
ReGAL's abstractions encapsulate frequently-used subroutines as well as
environment dynamics.

通过改善大型语言模型的抽象能力和预测的准确性，提出了一种名为 ReGAL 的梯度自由学习方法来重构代码并学习可复用函数库，从而在多个领域中提高程序预测的准确性。

ReGAL: 重构程序以发现可通用抽象

ReGAL: Refactoring Programs to Discover Generalizable Abstractions

This paper addresses the problem of learning abstractions that boost robot
planning performance while providing strong guarantees of reliability. Although
state-of-the-art hierarchical robot planning algorithms allow robots to
efficiently compute long-horizon motion plans for achieving user desired tasks,
these methods typically rely upon environment-dependent state and action
abstractions that need to be hand-designed by experts.
We present a new approach for bootstrapping the entire hierarchical planning
process. This allows us to compute abstract states and actions for new
environments automatically using the critical regions predicted by a deep
neural network with an auto-generated robot-specific architecture. We show that
the learned abstractions can be used with a novel multi-source bi-directional
hierarchical robot planning algorithm that is sound and probabilistically
complete. An extensive empirical evaluation on twenty different settings using
holonomic and non-holonomic robots shows that (a) our learned abstractions
provide the information necessary for efficient multi-source hierarchical
planning; and that (b) this approach of learning, abstractions, and planning
outperforms state-of-the-art baselines by nearly a factor of ten in terms of
planning time on test environments not seen during training.

本研究介绍了一种使用深度神经网络自动生成机器人特定体系结构进行环境预测和自动计算抽象状态和动作的方法，得到的结果可用于提高机器人的规划性能和可靠性。研究结果表明，所学习的抽象状态和动作可以与一种新型的多源双向分层机器人规划算法配合使用，在测试环境中的计划时间上的表现比现有技术的基准方法提高了近 10 倍。