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logprob measures how fluent it is to generate that token, which is not equivalent to how correct the fact is; a model can perfectly well generate a false statement fluently and with high probability, so it remains a relative signal rather than an absolute guarantee.
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10/22/2026
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UnverifiedAdvantage-sign-aware Renyi preprocessing is used to counteract saturation at the logit level, enabling gradient signals to propagate effectively in high-confidence regions and penalizing confident but incorrect predictions.71% similarUnverifiedlogprob thresholds need to be calibrated for the specific model and task distribution; directly applying a fixed threshold often works poorly.70% similarUnverifiedShannon pointed out that if an event has n equally probable possible outcomes, the minimum amount of information required to determine the specific outcome is log₂(n) bits.70% similarUnverifiedThere is no universal formula for choosing logit bias values; it is recommended to start with small adjustments, test the effect with representative prompts while watching for side effects, and then adjust gradually.69% similarUnverifiedLogprob mismatch is one of the fundamental causes of instability and poor reproducibility in many RL training runs; after achieving bit-for-bit alignment, the determinism and reproducibility of training improve significantly.68% similar
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