LinkDiogo AlmeidaTalk
Teams often struggle when deploying conversational models into autonomous pipelines because the underlying models were optimised to please human evaluators rather than execute tasks flawlessly without supervision. Recognising this architectural mismatch helps engineers choose the right post-training mechanisms, such as RLVR, when building dependable agentic systems.
Drawing on his experience building ChatGPT and InstructGPT, Almeida provides a grounded explanation for the paradox where AI solves complex math competitions yet remains untrustworthy for routine customer decisions. By distinguishing between assistance (where user satisfaction is part of the task) and automation (where silence and dependable execution are paramount), he highlights why RLHF-trained systems inherently resist unattended deployment without architectural redesign.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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