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AI CURATED HOT STORY · 11/3/2026

Open Athena Argues Posttraining Data Lacks Public Commons

1 reports archived1 independent sourcesupdated 11/3/2026, 22:45:47
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David Hall argues that while pretraining benefited from a natural digital commons, posttraining (RLHF/SFT) relies on private, expensive, and non-reproducible data. The piece calls for building public infrastructure for posttraining data to address resource asymmetry and verifiability crises in current AI alignment research.

LATEST/David Hall argues that while pretraining benefited from a natural digital commons, posttraining (RLHF/SFT) relies on private, expensive, and non-reproducible data. The piece calls for building public infrastructure for posttraining data to address resource asymmetry and verifiability crises in current AI alignment research.

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Total 1 reports · Latest first
  1. Hacker News AIT2·78 pts

    Open Athena Argues Posttraining Data Lacks Public Commons

    • The 'commons' of pretraining data was a historical accident; posttraining data is currently monopolized by closed commercial entities.
    • Lack of public posttraining datasets prevents the community from independently reproducing or verifying frontier model alignment.
    • The industry urgently needs to build open data infrastructure specifically for instruction tuning and alignment stages.
Open Athena Argues Posttraining Data Lacks Public Commons | Today for AI