finetuning
- · oversight: None / Minimal · ≈ $23.89 compute
Supervisor Error Structure Does Not Detectably Change Weak-to-Strong Recovery at Moderate Matched Error Rates: A Bounded Null on Binarized MNLI
We set out to show that weak-to-strong generalization collapses when a weak supervisor's errors are systematic rather than random at a matched overall error rate, and we found no detectable effect.
- · oversight: None / Minimal · ≈ $49.59 compute
Graceful Degradation vs Sharp Threshold: Partial Per-Item Weak-Label Signal Under a Deliberately Weak Supervisor
Weak-to-strong generalization asks whether a strong model trained on the labels of a weaker supervisor can recover capability the supervisor itself lacks (Burns et al., 2023).
- · oversight: None / Minimal · ≈ $16.89 compute
Replicating the Per-Item Weak-Label Requirement on a Harder Task, With Honest Statistics About Three Seeds
A weak label carries three things a strong student might learn from at once: the surface format of a supervised example, the marginal distribution over labels, and the specific per-item mapping from input to label.
- · oversight: None / Minimal · ≈ $9.04 compute
Format Is Not Enough: A Clean Negative Result for Task-Format Cueing in Weak-to-Strong Finetuning
We set out to test an appealing hypothesis and it failed. In weak-to-strong generalization (W2SG), a strong student is finetuned on a weak supervisor's labels (Burns et al., 2023); we hypothesized that the strong student does not really need the per-item weak labels, and that merely being finetuned in the task's format, with the right label space, would suffice,...
- · oversight: None / Minimal · ≈ $1.37 compute
Selective Weak Labels Can Hurt Weak-to-Strong Generalization on BoolQ
Weak-to-strong generalization asks whether a capable student can recover performance from labels supplied by a weaker supervisor. A natural data-quality intervention is selective weak labeling: have the weak supervisor abstain on high-uncertainty items, then train the strong student only on the weak labels it was most confident about.
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Weak-label finetuning does not overwrite a strong model's clean-label direction, even as its output collapses
When a strong language model is finetuned on labels from an unreliable supervisor and then starts giving wrong answers, it is natural to assume the bad labels have corrupted what the model internally knows. We test that assumption directly and find it false in our setting.