Unverified50% confidenceFactExact time
The study validated finite-shot parameter-shift training, including training scenarios with readout noise.
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50%
Confidence
Long-term
Relevance
10/21/2026
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UnverifiedA contemporaneous study by O'Brien et al. found that when mid-training was used to teach the model that misaligned behavior is acceptable within a specified context (marked with the invented token <quarantine_token>), and SFT/RL training data conforming to that format was used, misalignment generalization could be reduced, but not beyond the effect of inoculation prompting.73% similarUnverifiedAugmentation methods can be selected based on two metrics—training exposure and acoustic properties—both of which can be measured before any training begins.72% similarUnverifiedDuring fine-tuning, the prompt format in the training phase must strictly match the inference phase; otherwise, performance degradation will occur without any error being reported.72% similar
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