Numerous recent techniques for text style transfer characterize their
approaches as variants of reinforcement learning and preference optimization.
In this work, we consider the relationship between these approaches and a class
of optimization approaches developed primarily for (non-neural) statistical
machine translation, formerly known as 'tuning'. Inspired by these techniques
from the past, we improve upon established preference optimization approaches,
incorporating multiple iterations of exploration and optimization, and choosing
contrastive examples by following a 'hope' vs 'fear' sampling strategy.
Cognizant of the difference between machine translation and style transfer,
however, we further tailor our framework with a new pseudo-parallel generation
method and a dynamic weighted reward aggregation method to tackle the lack of
parallel data and the need for a multi-objective reward. We evaluate our model
on two commonly used text style transfer datasets. Through automatic and human
evaluation results we show the effectiveness and the superiority of our model
compared to state-of-the-art baselines.

通过引入探索和优化的多次迭代和希望与恐惧采样策略，以及伪平行生成方法和动态加权奖励聚合方法来改进偏好优化方法，本文在两个常用的文本风格转换数据集上评估模型，通过自动化和人工评估结果展示了我们模型相对于最先进基准的有效性和优越性。

多迭代偏好优化的风格转换

Style Transfer with Multi-iteration Preference Optimization

PARAGEN is a PyTorch-based NLP toolkit for further development on parallel
generation. PARAGEN provides thirteen types of customizable plugins, helping
users to experiment quickly with novel ideas across model architectures,
optimization, and learning strategies. We implement various features, such as
unlimited data loading and automatic model selection, to enhance its industrial
usage. ParaGen is now deployed to support various research and industry
applications at ByteDance. PARAGEN is available at
this https URL.

PARAGEN 是一个基于 PyTorch 的 NLP 工具包，提供 13 种可定制的插件，包括模型架构、优化和学习策略，并实现了无限数据加载和自动模型选择等各种功能，支持对 ByteDance 的各种研究和行业应用。

PARAGEN：一个并行生成工具包

PARAGEN : A Parallel Generation Toolkit

We introduce Autoregressive Diffusion Models (ARDMs), a model class
encompassing and generalizing order-agnostic autoregressive models (Uria et
al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we
show are special cases of ARDMs under mild assumptions. ARDMs are simple to
implement and easy to train. Unlike standard ARMs, they do not require causal
masking of model representations, and can be trained using an efficient
objective similar to modern probabilistic diffusion models that scales
favourably to highly-dimensional data. At test time, ARDMs support parallel
generation which can be adapted to fit any given generation budget. We find
that ARDMs require significantly fewer steps than discrete diffusion models to
attain the same performance. Finally, we apply ARDMs to lossless compression,
and show that they are uniquely suited to this task. Contrary to existing
approaches based on bits-back coding, ARDMs obtain compelling results not only
on complete datasets, but also on compressing single data points. Moreover,
this can be done using a modest number of network calls for (de)compression due
to the model's adaptable parallel generation.

本文介绍了 AR 模型中的一种 Autoregressive Diffusion Models，相较于其他模型具有更高的性能表现。同时，作者还将该模型应用于无损压缩，并表明该模型在此任务上也表现出了极佳的结果，其适用于数据点的压缩及适应各种生成任务。