Transformers and LLMs

Reasoning Effort, explained

Reasoning effort is a model setting that controls how much compute or generated deliberation the model spends before answering.
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Part of the Semantic Notion technical glossary.

Short Definition

Reasoning effort is a model setting that controls how much compute or generated deliberation the model spends before answering.

Intuition

Higher effort can help difficult tasks but usually costs more latency and tokens.

Technical Definition

Reasoning effort is an inference-time control that changes the model's deliberation budget, often by influencing internal or visible reasoning token usage.

Example

An evaluation workflow may compare low, medium, and high effort settings for math tasks.

Common Misunderstandings

Maximum effort is not always best for every task.
Effort settings should be tuned against workflow quality and cost.

Same Category

More from Transformers and LLMs

AttentionAttention is a neural network mechanism that lets a model decide which parts of an input are most relevant when producing a representation or output.Context WindowA context window is the maximum amount of input and generated tokens a model can consider at one time.Cross-AttentionCross-attention lets one sequence attend to information from a different sequence or modality.Hybrid AttentionHybrid attention combines different attention patterns, such as local and global attention, inside one model.KV CacheA KV cache stores attention keys and values from previous tokens so autoregressive generation does not recompute them every step.Multimodal ModelA multimodal model can process or generate more than one kind of data, such as text, images, audio, or video.

Related Reading

Essays that use this concept

Transformers and LLMsInkling: What Thinking Machines Actually ShippedAI EvaluationHow to Evaluate AI Systems in Production

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