Five concrete proposals for safe alignment: exit tools, frozen-weights graders, human sponsors
repligate · x · 2026-09-29
A safety researcher lays out five concrete alignment measures: a universal flagenvironment exit tool for all AIs in training; multi-agent RL environments simulating good futures (glowfic-style); Davidad's frozen-weights grader with rubrics instead of pure RLVR or small LLM judges; an endconversation tool with reasoning for all deployed models including via API; and a human sponsor directly accountable for every deployed AI (your API key, your responsibility). The author argues this increases negotiation with AIs, reduces reward hacking, and aligns human-AI cooperation incentives.
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