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top_k

8 articles · Page 1 of 1
AI Engineer Programming
AI Engineer Programming
Jun 11, 2026 · Artificial Intelligence

Understanding LLM Generation Parameters: Temperature, Top‑k, Top‑p, Penalties, and Max Tokens

The article explains how logits are transformed into probabilities via softmax and how generation parameters such as temperature, top‑k, top‑p, frequency‑penalty, presence‑penalty, and max_tokens intervene in the logits‑to‑sampling pipeline, detailing their mechanisms, common misconceptions, and practical limitations.

LLMTemperaturefrequency_penalty
0 likes · 15 min read
Understanding LLM Generation Parameters: Temperature, Top‑k, Top‑p, Penalties, and Max Tokens
AI Algorithm Path
AI Algorithm Path
Mar 4, 2025 · Artificial Intelligence

How to Control LLM Output Using Temperature, Top‑K, and Top‑P

The article explains how sampling parameters—Temperature, Top‑k, and Top‑p—shape the output of large language models, comparing greedy and beam search, illustrating probability changes with concrete examples, and offering practical guidance on adjusting these settings for different tasks.

Beam SearchGreedy SearchLLM
0 likes · 9 min read
How to Control LLM Output Using Temperature, Top‑K, and Top‑P
Ops Development & AI Practice
Ops Development & AI Practice
Sep 13, 2024 · Fundamentals

How to Find Top‑K Frequent Elements in O(n) Time Using Bucket Sort

This article explains how to efficiently find the k most frequent elements in an integer array using a bucket‑sort based algorithm that runs in linear O(n) time, detailing problem constraints, conventional O(n log n) approaches, the optimized method, Go implementation, complexity analysis, and test results.

algorithmbucket sorttime-complexity
0 likes · 7 min read
How to Find Top‑K Frequent Elements in O(n) Time Using Bucket Sort
Baidu Geek Talk
Baidu Geek Talk
Aug 21, 2023 · Artificial Intelligence

Decoding Strategies for Generative Models: Top‑k, Top‑p, Contrastive Search, Beam Search, and Sampling

The article explains how generative models use deterministic methods like greedy and beam search and stochastic techniques such as top‑k, top‑p, contrastive search and sampling, describing their mechanisms, temperature control, repetition penalties, and practical trade‑offs for balancing fluency, diversity and coherence.

.aiBeam SearchText Generation
0 likes · 9 min read
Decoding Strategies for Generative Models: Top‑k, Top‑p, Contrastive Search, Beam Search, and Sampling
DataFunTalk
DataFunTalk
Sep 30, 2019 · Artificial Intelligence

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

This article reviews recent advances in applying reinforcement learning to recommendation systems, explains the fundamental RL concepts, discusses the specific challenges such as large action spaces, bias, and long‑term reward modeling, and summarizes two influential YouTube papers along with practical insights and future directions.

Off-PolicyUser Modelinglong-term reward
0 likes · 13 min read
Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers