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AI CLUSTERED EVENT · 10/9/2026

Ex-OpenAI Researcher Proposes RLCD Framework to Bridge Gap Between LLM Intelligence and Engineering Reliability

1 reports archived1 independent sourcesupdated 10/9/2026, 17:55:11
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Diogo Almeida, former OpenAI researcher and TypeSafe CEO, introduces the Jev project to address the reliability gap in current AI coding assistants like Codex and Claude Code. He proposes Reinforcement Learning for Calibrated Decisions (RLCD), shifting training objectives from human preference alignment to generating machine-consumable outputs including choices, scores, and probabilities. This approach aims to quantify uncertainty, enabling developers to orchestrate human-AI workflows based on confidence thresholds and transform AI into a foundational software capability.

LATEST/Diogo Almeida, former OpenAI researcher and TypeSafe CEO, introduces the Jev project to address the reliability gap in current AI coding assistants like Codex and Claude Code. He proposes Reinforcement Learning for Calibrated Decisions (RLCD), shifting training objectives from human preference alignment to generating machine-consumable outputs including choices, scores, and probabilities. This approach aims to quantify uncertainty, enabling developers to orchestrate human-AI workflows based on confidence thresholds and transform AI into a foundational software capability.

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  1. InfoQ 中文T2·68 pts

    Ex-OpenAI Researcher Proposes RLCD Framework to Bridge Gap Between LLM Intelligence and Engineering Reliability

    Original: Codex 和 Claude Code 都跑偏了，前 OpenAI 研究员称 Jev 出现前 AI 世界是个悲剧

    • Diogo Almeida argues that current AI coding assistants (Codex/Claude Code) remain in a 'human-led, AI-assisted' phase, failing to achieve true automation loops.
    • The RLCD framework proposed by Jev focuses on outputting structured data with probabilities and scores, allowing code to decide when to execute or escalate to humans.
    • This view challenges mainstream RLHF paradigms, suggesting that aligning with user preferences may hide true model confidence, hindering deterministic automation.