6 papers
Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
Valery Parfenov, Grigoriy Evseev, Andrey Veprikov +3
Fine-tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation and large optimizer States) limit depl…
TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance
Elena Bruches, Vadim Alperovich, Dari Baturova +8
While Large Language Models (LLMs) have shown promise in software engineering, their application to unit testing remains largely confined to isolated test generation or oracle pred…
RM -RF: Reward Model for Run-Free Unit Test Evaluation
Elena Bruches, Daniil Grebenkin, Mikhail Klementev +8
We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF pre…
MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Artem Chervyakov, Alexander Kharitonov, Pavel Zadorozhny +20
Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most b…
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
Egor Petrov, Grigoriy Evseev, Aleksey Antonov +4
Fine-tuning Large Language Models (LLMs) is essential for adapting pre-trained models to downstream tasks. Yet traditional first-order optimizers such as Stochastic Gradient Descen…
Targeted Test Selection Approach in Continuous Integration
Pavel Plyusnin, Aleksey Antonov, Vasilii Ermakov +4
In modern software development change-based testing plays a crucial role. However, as codebases expand and test suites grow, efficiently managing the testing process becomes increa…