Records found: 4
'W4S trains a 7B meta-agent to program Python workflows that call stronger LLM executors, using offline RL to iteratively generate, execute, and refine solutions. The approach yields consistent gains across 11 benchmarks and achieves Pass@1 of 95.4 on HumanEval with GPT-4o-mini.'
'RA3 formalizes mid-training as pruning plus horizon shortening and uses temporal action abstractions to accelerate RL post-training, boosting code generation benchmarks.'
Explore the comprehensive 2025 benchmarks and metrics evaluating top coding large language models, highlighting key performers like OpenAI, Gemini, and Anthropic in real-world developer scenarios.
EG-CFG introduces real-time execution feedback into code generation, significantly improving performance on major benchmarks and surpassing leading models like GPT-4.