Comparison
Beam Search vs Greedy Decoding
Beam Search and Greedy Decoding are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Beam Search
Beam Search comes up when the question is fundamentally about inference.
Translation systems with beam width 4-10.
When you would reach for Greedy Decoding
Greedy Decoding comes up when the question is fundamentally about inference.
Asking a model "What is 2+2?" — greedy is fine.
Frequently asked
What is the difference between Beam Search and Greedy Decoding?
Beam Search: Beam search explores several candidate continuations in parallel, keeping the top-k partial sequences at each step. Common in translation; rare in modern LLM chat. Greedy Decoding: Greedy decoding always picks the single highest-probability next token. It is deterministic, fast, and often dull.
When should I use Beam Search vs Greedy Decoding?
Beam Search is the right concept when you are focused on inference. Greedy Decoding applies when you are focused on inference.
Are Beam Search and Greedy Decoding the same thing?
No. Beam Search is inference; Greedy Decoding is inference. They are related but address different parts of the AI stack.