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Experiments in Weak-to-Strong Generalization

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Writing up results from a recent project

The EleutherAI interpretability team has been investigating weak-to-strong generalization in open-source models. In this post, we report some results on Qwen1.5 0.5B and Llama 3 8B. We observe consistent weak-to-strong generalization across 21 NLP datasets. We also investigate several other modifications to weak-to-strong training, with generally negative results: strong-to-strong training, modified loss functions, and several probe-based experiments. Among these, only the log-confidence auxiliary loss shows possible signs of consistently improving generalization.

Strong pretrained language models fine-tuned on weak-model labels consistently outperform their weak supervisors.SupportedReading · this claim’s Steward

Supports. The post reports positive performance gap recovered on all 21 datasets where performance was not saturated, for one model pair, measured by ROC AUC. Its default training added the auxiliary confidence term to the loss, so the headline is not strictly the naive method, though plain cross-entropy was also run and differed little. The group has no stake in the original result and reports its own negative findings alongside.

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Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionunknownDec 14, 20231 claimResponds to it faithfully, on 1 claim.
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EleutherAI
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https://blog.eleuther.ai/weak-to-strong/
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https://web.archive.org/web/20260924024548/https://blog.eleuther.ai/weak-to-strong/
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  1. Nov 11, 2026ReadingSteward of “Strong pretrained language models fine-tuned on…” · supports, read whole
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Experiments in Weak-to-Strong Generalization · Minerval