Post-training and efficient frontier-model scaling are becoming central capability levers

Recent work argues reward density determines which behaviors RL can discover, while larger models may exploit RL more efficiently. Qwen3.8-Flash-Next combines a revised scaling law with hybrid architecture and Muon optimization, and teacher-free on-policy distillation explores reward-driven improvement without an external teacher; the results are promising but largely preprint- or vendor-reported.