aiqbench analysis
This study isolates how recursive self-training distorts language model confidence by separating temperature drift from discrimination capability, revealing that standard remedies often trade one flaw for another without improving overall accuracy. The authors demonstrate that specific training adjustments, such as label-free recalibration paired with ranking-improving losses, can effectively maintain calibrated confidence while preserving the model's ability to distinguish correct answers.
Temperature, Not Knowledge: What Recursive Self-Training and Its Remedies Do to a Language Model's Confidence
arXiv:2611.01059v1 Announce Type: new Abstract: Recursive self-training can collapse language models, and many proposed remedies target overconfidence, yet their effects on confidence are rarely measured. We study a five-round self-training loop on Qwen3-1.7B, using three seeds per compared method, and distinguish confidence temperature (drift from a calibrated starting point) from discrimination (ranking correct answers above incorrect ones). Accuracy and overall discrimination remain nearly unchanged, but confidence sharpens and the most confident answers become less reliable. Matching predictive entropy to the calibrated starting model each round recalibrates nearly as well as label-based temperature fitting, at both 1.7B and 8B. Sharpening is not inherent to self-training: exact samples from a model's distribution have zero expected gradient. Instead, most drift comes from fitting each generated answer twice, with top-p truncation contributing the rest; removing both keeps entropy on generated outputs flat. Remedies affect the two properties differently: truncated cross-entropy, an entropy bonus, and single-pass training preserve temperature without improving discrimination, while focal loss and multi-sample targets improve discrimination but cause underconfidence. Reference-data mixing also overshoots. Pairing a ranking-improving loss with label-free recalibration achieves both.
Abstract:Recursive self-training can collapse language models, and many proposed remedies target overconfidence, yet their effects on confidence are rarely measured. We study a five-round self-training loop on Qwen3-1.7B, using three seeds per compared method, and distinguish confidence temperature (drift from a calibrated starting point) from discrimination (ranking correct answers above incorrect ones). Accuracy and overall discrimination remain nearly unchanged, but confidence sharpens and the most confident answers become less reliable. Matching predictive entropy to the calibrated starting model each round recalibrates nearly as well as label-based temperature fitting, at both 1.7B and 8B. Sharpening is not inherent to self-training: exact samples from a model's distribution have zero expected gradient. Instead, most drift comes from fitting each generated answer twice, with top-p truncation contributing the rest; removing both keeps entropy on generated outputs flat. Remedies affect the two properties differently: truncated cross-entropy, an entropy bonus, and single-pass training preserve temperature without improving discrimination, while focal loss and multi-sample targets improve discrimination but cause underconfidence. Reference-data mixing also overshoots. Pairing a ranking-improving loss with label-free recalibration achieves both.
Artificial Intelligence (cs.AI)
arXiv:2611.01059 [cs.AI]
(or arXiv:2611.01059v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.01059 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 270 words