Repetition Loops, Hallucination Spikes, and the Hard Limits of Sampling Parameter Tuning
Wrong sampling parameters trap LLMs in repetition loops or hallucination. Trace the probability math behind both failure modes and the fixes that actually work.
Temperature and Sampling
Temperature and sampling are the parameters that control how a large language model selects its next token during text generation.
Temperature scales the probability distribution over candidate tokens, making outputs more deterministic at low values and more creative at high values. Complementary methods like top-k, top-p (nucleus sampling), and min-p further constrain which tokens the model considers. Together these settings let practitioners balance coherence, diversity, and factual reliability for any given use case. Also known as: Sampling Strategies, Decoding Strategies.
What this topic covers
- Foundations — Temperature and sampling sit at the boundary between a model's learned knowledge and the text it actually produces.
- Implementation — These guides walk through choosing and configuring temperature, top-p, and min-p across real workloads, from deterministic extraction pipelines to open-ended creative generation.
- What's changing — Sampling defaults are shifting fast as providers lock parameters, adopt min-p, and move toward adaptive decoding.
- Risks & limits — Opaque default settings and locked sampling controls raise questions about user autonomy, output accountability, and the hidden influence of provider-chosen parameters on downstream decisions.
This topic is curated by our AI council — see how it works.
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Understand the Fundamentals
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Top-K, Top-P, Min-P, and Beam Search: Every LLM Sampling Method Compared
Compare top-k, top-p, min-p, and beam search LLM sampling methods. Learn how each reshapes probability distributions and how they interact in API calls.
What Is Temperature in LLMs and How Softmax Scaling Controls Text Generation Randomness
Temperature divides logits before softmax, reshaping the token probability distribution. Learn how this parameter, top-p, and min-p control LLM randomness.
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Build with Temperature and Sampling
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
How to Choose and Configure Temperature, Top-P, and Min-P for Every LLM Use Case in 2026
Configure temperature, top-p, and min-p for code generation, creative writing, and RAG pipelines across OpenAI, Anthropic, llama.cpp, and vLLM in 2026.
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What's Changing in 2026
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated March 2026
Locked Temperatures, Min-P Adoption, and the Sampling Parameter Shifts Reshaping LLMs in 2026
OpenAI locked temperature on reasoning models. Open-source stacks adopted min-p. The sampling parameter surface developers relied on is splitting in two.
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Risks and Considerations
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Opaque Defaults and Locked Knobs: The Ethics of Who Controls LLM Sampling Parameters
Major LLM providers are locking sampling parameters like temperature and top-p. Explore who controls these defaults, what biases they encode, and why it matters.