Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Yes — let’s go carefully and clarify both points. ===
Yes — let’s go carefully and clarify both points.

==== - Top-k = 1 → the model always picks the single highest-scoring token. ====
* Temperature very low (→0) → the model leans strongly toward the highest-scoring token, but if top-k > 1, it could still pick a lower-scoring token (very rarely).

✅ So your intuition is mostly correct: top-k = 1 is like forcing the model to always pick the strongest path, which is conceptually similar to minimal temperature.
* Difference: - Temperature = “how strongly the model favors the top token over others” (soft weighting) - Top-k = “ignore all but the top k tokens entirely” (hard cutoff)

===== - Imagine the model ranks tokens by their scores: token1 = 0.5, token2 = 0.3, token3 = 0.1, token4 = 0.05, token5 = 0.05. =====
* Cumulative probability = sum of top probabilities until reaching threshold p.
* Example: top-p = 0.8 → pick only tokens whose cumulative probability ≤ 0.8 → here token1 + token2 = 0.5 + 0.3 = 0.8 → only these two tokens can be chosen.

: 

==== - Yes, minimal temperature + fixed top-k/top-p can create repeatable, deterministic outputs for a given prompt. ====
* Important caveat: - If the model is heavily quantized, tiny rounding differences in activations may still produce different outputs, even at low temperature. - So “perfect repeatability” is easier on high-precision models (32-bit or 16-bit weights) than on 8-bit or 4-bit quantized ones.
* In practice: - Low temperature → reduces randomness → mostly repeatable - Fixed top-k/top-p → further guarantees that only certain paths are considered

===== - Top-k = 1 + minimal temperature → closest to deterministic token selection =====
* Top-p → probabilistic cutoff that adapts to token distribution
* Quantization and floating-point noise → can still introduce tiny variability even at “deterministic” settings

If you want, I can make a diagram showing top-k vs top-p vs temperature affecting which neuron paths are selected, which makes the difference visually clear.

Do you want me to do that?