Qwen3.8-27B RL Post-Train Cuts Hallucination Rate in Half
The notable result is efficiency: matching frontier models on agent benchmarks while emitting far fewer tokens per task, which directly cuts inference cost for agentic workflows. Developers running local agent stacks can fit this open model on one RTX 4090 and get longer sessions via the MLA version. Teams choosing between open small models and GPT-6 Luna now have benchmark-level evidence that post-trained Qwen variants compete on most agent tasks.
