Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning

Nagham Omar, Maya R, Evgeny Mishlyakov, Avigdor Gal

Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning: 5 upvotes on Hugging Face Daily Papers, #26 of 32 papers on 2026-09-10. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learning. We show that this choice has exploitable structure: retraining is most useful in early rounds, when each batch can substantially reshape the labeled distribution, while fine-tuning becomes safer once the model trajectory stabilizes. We propose HybridAL, an adaptive training schedule that monitors an online stabilization signal and switches from retraining to fine-tuning after sustained stabilization. Two complementary signals, spectral exponent change Δα (weight-based) and accuracy change ΔAcc (validation-based), span different points on the time-calibration trade-off. Across three encoder backbones and six text-classification tasks (five seeds each), HybridAL keeps endpoint macro-F1 non-inferior to retraining and fine-tuning at a 0.010 margin, saves up to 49% of retraining time, and recovers a substantial fraction of retraining's calibration advantage as measured by negative log-likelihood (NLL). Compared with schedules that switch at a pre-committed round, HybridAL obtains lower NLL at moderate additional cost, showing that trajectory-dependent switching provides a stronger time-calibration trade-off than fixed early switching.

Paper page on Hugging Face · arXiv

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