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RLHF is not a tax on capability: preference tuning also raises maths and code scores, which is why the labs keep reaching for it.

Drawn from what Nathan Lambert said

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reinforcement learning Training by reward: environments, verifiable tasks, value functions, and whether it works or merely beats what came before.

What Nathan Lambert actually said

  1. Nathan Lambert Machine-learning researcher on open language models
    And the important thing to say is that no matter how you want the model to behave, these RLHF and preference-tuning techniques also improve performance. So, on things like math evals and code evals, there is something innate to these, what is called contrastive loss functions.

Added to korrents 7 Sept 2026 · How quotes work · Something wrong? Tell us

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