Meta and UIUC Train 8B Model via EvoHarness-RL to Match Claude Opus 4.5 on Agentic Workflows
Researchers from Meta AI and the University of Illinois Urbana-Champaign have introduced EvoHarness-RL, a framework designed to teach language models how to dynamically manage external agent harnesses during long-horizon tasks. In experiments on the sequential reasoning benchmark ALFWorld, a Qwen3-8B model trained with EvoHarness-RL attained a 96.9% task success rate. The result represents a 49.0 percentage point gain over baseline ReAct prompting (47.9%), outperforms specialized trainable agen
















