SkelDPO improves code generation efficiency by 2-7% over prior DPO methods via joint preference losses on full code and efficiency-critical skeletons.
Evaluating language models for efficient code generation
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
representative citing papers
CppPerf-Mine produces CppPerf-DB, a benchmark of 347 real-world performance-improving C++ patches (39% multi-file) from 42 repositories to evaluate repository-level repair tools.
LLMs propose volatile performance improvements on real-world Java tasks that lag human developers on average, showing algorithmic benchmarks overestimate capabilities.
JETO-Mine is a reusable three-phase pipeline that mines 1.8 million Java commits to produce JETO-Bench containing 91 verified executable ETIPs, on which OpenHands succeeds at 14.3%.
EffiSkel improves LLM-generated code efficiency by supervising on extracted structural efficiency skeletons via multi-task learning of code generation and skeleton prediction.
ConVer decomposes C program verification top-down by synthesizing contracts with LLMs and refining them in a CEGAR-CEGIS loop, reporting 82-96% success on simple benchmarks and lower rates on harder suites.
MONA integrates Nesterov acceleration into Muon's orthogonalization framework, reporting better convergence than Muon and AdamW on MoE models up to 68B parameters trained on 1T tokens and SOTA fine-tuning results.
SysLLMatic integrates LLMs with performance diagnostics and a 43-pattern catalog to optimize complex software, reporting 1.54x latency and 1.24x energy gains over compilers on large Java systems where prior LLM methods did not scale.
citing papers explorer
-
JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java
JETO-Mine is a reusable three-phase pipeline that mines 1.8 million Java commits to produce JETO-Bench containing 91 verified executable ETIPs, on which OpenHands succeeds at 14.3%.