A Deep Learning Model for Predicting Transformation Legality

• URL: http://arxiv.org/abs/2511.06120v1
• Date: Sat, 08 Nov 2025 20:08:16 GMT
• Title: A Deep Learning Model for Predicting Transformation Legality
• Authors: Avani Tiwari, Yacine Hakimi, Riyadh Baghdadi,
• Abstract summary: We propose a novel DL model for predicting the legality of transformations.<n>The model takes the code representation and a list of transformations as input and predicts whether applying those transformations to the code is legal.<n>We demonstrate that such a replacement enables the agent to train on twice as many steps, resulting in faster training and reducing resource usage.
• Score: 0.9558392439655014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compilers must check the legality of code transformations to guarantee the correctness of applying a sequence of code transformations to a given code. While such a legality check needs to be precisely computed in general, we can use an approximate legality prediction model in certain cases, such as training a reinforcement learning (RL) agent for schedule prediction. In this paper, we propose an approximate method for legality checks. We propose a novel DL model for predicting the legality of transformations. The model takes the code representation and a list of transformations as input and predicts whether applying those transformations to the code is legal. We implement and evaluate the proposed model, demonstrating its effectiveness. Our evaluation shows an F1 score of 0.91 on a test set of randomly generated programs. To further evaluate the model in a practical scenario, we used the model to replace the legality check used during the training of an RL agent designed for automatic code optimization. We demonstrate that such a replacement enables the agent to train on twice as many steps, resulting in faster training and reducing resource usage by approximately 80\% for CPU and 35\% for RAM. The agent trained using this approach maintains comparable performance, with only a 4\% reduction on benchmarks from the Polybench suite compared to the traditional method.

Related papers

• Reinforcing General Reasoning without Verifiers [47.72684162518086]
  We propose a verifier-free method (VeriFree) that bypasses answer verification and instead uses RL to directly maximize the probability of generating the reference answer.<n>VeriFree matches and even surpasses verifier-based methods on extensive evaluations across MMLU-Pro, GPQA, SuperGPQA, and math-related benchmarks.
  arXiv  Detail & Related papers  (2025-05-27T17:56:27Z)
• InfAlign: Inference-aware language model alignment [58.66389179049758]
  Language model alignment is a critical step in training modern generative language models.<n>We show that this train/test mismatch makes standard RLHF framework sub-optimal in view of inference-time methods.<n>We propose a framework for inference-aware alignment (InfAlign), which aims to optimize inference-time win rate of the aligned policy against the base model.
  arXiv  Detail & Related papers  (2024-12-27T18:45:36Z)
• Test-Time Model Adaptation with Only Forward Passes [68.11784295706995]
  Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. We propose a test-time Forward-Optimization Adaptation (FOA) method. FOA runs on quantized 8-bit ViT, outperforms gradient-based TENT on full-precision 32-bit ViT, and achieves an up to 24-fold memory reduction on ImageNet-C.
  arXiv  Detail & Related papers  (2024-04-02T05:34:33Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning [105.70602423944148]
  We propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a $Q$-value head on both states and obtains two distributions of action values. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.
  arXiv  Detail & Related papers  (2022-06-25T03:02:25Z)
• Proof-of-Learning: Definitions and Practice [15.585184189361486]
  Training machine learning (ML) models typically involves expensive iterative optimization. There is currently no mechanism for the entity which trained the model to prove that these parameters were indeed the result of this optimization procedure. This paper introduces the concept of proof-of-learning in ML.
  arXiv  Detail & Related papers  (2021-03-09T18:59:54Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• The Right Tool for the Job: Matching Model and Instance Complexities [62.95183777679024]
  As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. We propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) "exit" We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks.
  arXiv  Detail & Related papers  (2020-04-16T04:28:08Z)
• Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift [8.068725688880772]
  Most predictive models assume that training and test data are generated from a stationary process. We consider the scenario of a gradual concept drift due to the underlying non-stationarity of the data source. We propose a novel, iterative algorithm for unsupervised adaptation of predictive models.
  arXiv  Detail & Related papers  (2020-02-10T21:29:09Z)
• Learning the Relation between Code Features and Code Transforms with Structured Prediction [13.62633524166298]
  We present the first approach for structurally predicting code transforms at the level of AST nodes using conditional random fields (CRFs) Our approach first learns offline a probabilistic model that captures how certain code transforms are applied to certain AST nodes, and then uses the learned model to predict transforms for arbitrary new, unseen code snippets.
  arXiv  Detail & Related papers  (2019-07-22T12:42:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.