Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code
executions, a challenging task due to the open-ended nature of programming
languages. While prior methods have resorted to heavy and domain-specific
feature engineering, we show that a single unified Regression Language Model
(RLM) can simultaneously predict directly from text, (i) the memory footprint
of code across multiple high-level languages such as Python and C++, (ii) the
latency of Triton GPU kernels, and (iii) the accuracy and speed of trained
neural networks represented in ONNX. In particular, a relatively small 300M
parameter RLM initialized from T5Gemma, obtains > 0.9 Spearman-rank on
competitive programming submissions from APPS, and a single unified model
achieves > 0.5 average Spearman-rank across 17 separate languages from CodeNet.
Furthermore, the RLM can obtain the highest average Kendall-Tau of 0.46 on five
classic NAS design spaces previously dominated by graph neural networks, and
simultaneously predict architecture latencies on numerous hardware platforms.
68dc92b64159d1f2418f9ae2
Yash Akhauri
claimed_verified
2025-10-01T15:29:22.497Z
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/avatars/c9d0edf7a4976a2b1dd8f191e84c3da1.svg
YASH AKHAURI
user
akhauriyash
68dc92b64159d1f2418f9ae3
Xingyou Song
68dc92b64159d1f2418f9ae4
Arissa Wongpanich
68dc92b64159d1f2418f9ae5
Bryan Lewandowski
68dc92b64159d1f2418f9ae6
Mohamed S. Abdelfattah
Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code
executions, a challenging task due to the open-ended nature of programming
languages. While prior methods have resorted to heavy and domain-specific
feature engineering, we show that a single unified Regression Language Model
(RLM) can simultaneously predict directly from text, (i) the memory footprint
of code across multiple high-level languages such as Python and C++, (ii) the
latency of Triton GPU kernels, and (iii) the accuracy and speed of trained
neural networks represented in ONNX. In particular, a relatively small 300M
parameter RLM initialized from T5Gemma, obtains > 0.9 Spearman-rank on
competitive programming submissions from APPS, and a single unified model
achieves > 0.5 average Spearman-rank across 17 separate languages from CodeNet.
Furthermore, the RLM can obtain the highest average Kendall-Tau of 0.46 on five
classic NAS design spaces previously dominated by graph neural networks, and
simultaneously predict architecture latencies on numerous hardware platforms.
A unified Regression Language Model (RLM) predicts numeric outcomes of code executions, including memory footprint, latency, and neural network performance, across multiple languages and hardware platforms.
Regression Language Model (RLM) T5Gemma Spearman-rank Kendall-Tau NAS design spaces graph neural networks