Min-p sampling is the probability-threshold decoding method that keeps tokens whose probability exceeds a dynamic minimum relative to the top token - it adapts candidate set size to local confidence conditions.

What Is Min-p sampling?

• Definition: Adaptive filtering strategy based on a minimum probability floor tied to peak likelihood.
• Mechanism: Tokens below threshold are removed, then remaining probabilities are renormalized for sampling.
• Adaptive Behavior: High-confidence steps keep small candidate sets, uncertain steps keep broader sets.
• Relation: Acts as an alternative to fixed top-k or fixed cumulative-mass truncation.

Why Min-p sampling Matters

• Context Sensitivity: Candidate filtering automatically adjusts to entropy changes across steps.
• Quality Control: Suppresses extreme tail tokens that often degrade coherence.
• Diversity Preservation: Retains multiple options when model uncertainty is genuinely high.
• Operational Simplicity: Single threshold parameter can replace multiple manual limits.
• Robustness: Often stabilizes generation across heterogeneous prompt types.

How It Is Used in Practice

• Threshold Calibration: Tune min-p values on factuality, coherence, and diversity benchmarks.
• Joint Policies: Combine with temperature controls for finer stochastic behavior shaping.
• Live Monitoring: Track candidate-set size distribution to detect over- or under-filtering.

Min-p sampling is an adaptive truncation strategy for stable stochastic decoding - min-p improves control by aligning candidate breadth with model confidence.