Large language models generate code one token at a time. Their autoregressive
generation process lacks the feedback of observing the program's output.
Training LLMs to suggest edits directly can be challenging due to the scarcity
of rich edit data. To address these problems, we propose neural diffusion
models that operate on syntax trees of any context-free grammar. Similar to
image diffusion models, our method also inverts ``noise'' applied to syntax
trees. Rather than generating code sequentially, we iteratively edit it while
preserving syntactic validity, which makes it easy to combine this neural model
with search. We apply our approach to inverse graphics tasks, where our model
learns to convert images into programs that produce those images. Combined with
search, our model is able to write graphics programs, see the execution result,
and debug them to meet the required specifications. We additionally show how
our system can write graphics programs for hand-drawn sketches.

大型语言模型通过对语法树进行迭代编辑，结合搜索以满足规定的规范，将图像转化为程序，并能够输出执行结果进行调试。

语法树扩散在程序合成中的应用

Diffusion On Syntax Trees For Program Synthesis

Advances in deep learning have led to promising progress in inferring
graphics programs by de-rendering computer-generated images. However, current
methods do not explore which decoding methods lead to better inductive bias for
inferring graphics programs. In our work, we first explore the effectiveness of
LSTM-RNN versus Transformer networks as decoders for order-independent graphics
programs. Since these are sequence models, we must choose an ordering of the
objects in the graphics programs for likelihood training. We found that the
LSTM performance was highly sensitive to the sequence ordering (random order
vs. pattern-based order), while Transformer performance was roughly independent
of the sequence ordering. Further, we present a policy gradient based
reinforcement learning approach for better inductive bias in the decoder via
multiple diverse rewards based both on the graphics program specification and
the rendered image. We also explore the combination of these complementary
rewards. We achieve state-of-the-art results on two graphics program generation
datasets.

本研究探讨了使用 LSTM-RNN 与 Transformer 网络作为解码器对无序图形程序进行识别的有效性，以及使用具有多样性奖励的强化学习方法来增强解码器的感知偏差，并在两个图形程序生成数据集上实现了最先进的结果。