SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
Meta large language model compiler: Foundation models of compiler optimization,
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EggMind automates EqSat strategy synthesis via LLMs and EqSatL, cutting final cost 45.1% and peak RAM 69.1% versus full equality saturation on vectorization benchmarks while transferring to tensor compilers.
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
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SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
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LLM-Guided Strategy Synthesis for Scalable Equality Saturation
EggMind automates EqSat strategy synthesis via LLMs and EqSatL, cutting final cost 45.1% and peak RAM 69.1% versus full equality saturation on vectorization benchmarks while transferring to tensor compilers.
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SysLLMatic: Large Language Models are Software System Optimizers
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.