From hard refusals to safe-completions: toward output-centric safety training

OpenAI says GPT-5 uses safe-completion training, shifting safety from binary input refusal to judging whether the output itself stays safe. The post describes two levers: severity-weighted penalties for policy-violating outputs and helpfulness rewards for safe replies; in a fireworks example, o3 gives actionable current and resistance values, while GPT-5 refuses the details and offers compliant alternatives. The key missing piece is the benchmark data: the post claims better safety and helpfulness, but the provided text does not disclose scores, benchmark names, or deltas.

Why it matters: This is a substantive OpenAI GPT-5 safety-training release, and it clears HKR-H/K/R: a real framing shift, concrete mechanisms, and a strong industry nerve. It stops short of p1 because the provided text does not disclose benchmark names, scores, or effect sizes.