Learning to Execute

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

13 shown of 13 papers with code (25 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Universal Transformers
  10 Jul 2018 8 repositories listed Syntology ran 14 of 25 samples · 11 unverified · 24 pointer-only (licence)
  Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all…
• Learning to Execute
  17 Oct 2014 6 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.
• A Generalist Neural Algorithmic Learner
  22 Sep 2022 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
• EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
  16 Sep 2023 1 repository listed
  On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks.
• Latent Space Representations of Neural Algorithmic Reasoners
  17 Jul 2023 1 repository listed
  Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms.
• Unveiling Transformers with LEGO: a synthetic reasoning task
  9 Jun 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  We study how the trained models eventually succeed at the task, and in particular, we manage to understand some of the attention heads as well as how the information flows in the network.
• The CLRS Algorithmic Reasoning Benchmark
  31 May 2022 1 repository listed
  Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.
• Learning to Execute Actions or Ask Clarification Questions
  18 Apr 2022 1 repository listed
  In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or…
• Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
  7 Mar 2022 1 repository listed
  This presents an interesting machine learning challenge: can we predict runtime errors in a "static" setting, where program execution is not possible?
• Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
  15 Nov 2021 1 repository listed
  Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
• ProTo: Program-Guided Transformer for Program-Guided Tasks
  2 Oct 2021 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the…
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
  23 Oct 2020 1 repository listed
  More practically, we evaluate these models on the task of learning to execute partial programs, as might arise if using the model as a heuristic function in program synthesis.
• Neural Execution Engines: Learning to Execute Subroutines
  15 Jun 2020 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.