RL on realistic scenarios boosts alignment across dozens of benchmarks
Researchers show that reinforcement learning on realistic tasks targeting helpful traits yields wide‑range alignment gains that hold up under adversarial pressure and across new domains. This suggests a concrete training path for models that stay honest, safe and useful even in unseen, high‑stakes settings.