Comparison, in AI in Markets
Greedy Decoding vs Beam Search: Which to Use
Decoding turns a model's per-token probabilities into an output sequence, and the strategy affects determinism, quality, and cost. Greedy decoding commits to the most likely token at every step, never reconsidering. Beam search hedges by tracking several partial sequences, or beams, and at each step keeps the top combinations by cumulative probability, so it can recover from a locally tempting but globally worse choice. Both are deterministic, unlike temperature sampling. For the short, structured outputs typical of filing extraction, the practical difference is smaller than for long open-ended generation. This matrix compares them.
9 criteria. Published May 26, 2026. By AI Fin Hub Research.
Selects the single highest-probability token at each step with no lookahead. Deterministic, fast, and the simplest decoding strategy.
Pros
- Fast and cheap: one forward path, no extra candidate sequences to maintain
- Deterministic, giving reproducible output that aids debugging and auditing
- Adequate for short, structured extraction where the high-probability path is usually correct
- Simple to implement and reason about, the default for many extraction pipelines
Cons
- Myopic: a locally best token can lead into a globally worse sequence with no recovery
- Can get stuck in repetition loops on open-ended generation
- No exploration of alternative phrasings, which occasionally matters for fluency
- Cannot enforce a globally higher-probability sequence the way beam search can
Short structured extraction, deterministic and auditable output, and high-volume pipelines where speed and reproducibility matter
Maintains several candidate sequences in parallel and expands the highest cumulative-probability combinations, choosing the best overall sequence at the end.
Pros
- Finds higher-probability sequences than greedy by considering several paths at once
- Recovers from a locally tempting token that greedy would commit to and regret
- Deterministic and useful for constrained generation where the global sequence matters
- Beam width tunes the quality-versus-cost tradeoff explicitly
Cons
- Several times the compute of greedy, scaling with the beam width
- Tends toward bland, safe, or repetitive output, the well-known beam-search degeneration
- Higher probability is not always better text, so it can underperform on open generation
- Rarely helps for short structured extraction where the greedy path is already correct
Constrained generation and tasks where the globally most probable sequence matters, less so short structured extraction
Decision table
See the tradeoffs side by side
| Criterion | Greedy Decoding | Beam Search |
|---|---|---|
| Lookahead | None, token by token | Keeps several candidate paths |
| Determinism | Yes | Yes |
| Compute cost | Low | Higher, scales with beam width |
| Finds global optimum | No, myopic | Closer, still approximate |
| Output tendency | Direct, can loop | Bland, can repeat |
| Fit for short extraction | Good default | Rarely worth the cost |
| Latency per token | One candidate, fastest path | Several candidates per step, slower |
| Tunable width | Fixed, width of one | Beam width trades quality for cost |
| Relation to sampling | Equivalent to temperature zero | Search, not sampling |
Verdict
For the extraction tasks that dominate finance LLM work, short structured outputs like a number, a date, or a small JSON object, greedy decoding at temperature zero is the right default: it is fast, deterministic, reproducible for auditing, and on these tasks the highest-probability path is almost always the correct one, so beam search's global search buys little. Beam search is genuinely useful when the globally most probable sequence matters and a single greedy misstep would derail the whole output, as in constrained generation, but it costs several times the compute, and its well-documented tendency to produce bland or repetitive text means higher sequence probability does not always mean better output. So reserve beam search for the narrow cases where global optimality clearly helps, and use greedy for everything else. Note that both are deterministic and distinct from temperature sampling and self-consistency, which deliberately introduce randomness to explore reasoning diversity; if you want exploration, that is a different axis than greedy versus beam.
Run the numbers next
- Calculators Token-Cost Optimizer
Compute the dollar cost of a trading research loop across Claude, GPT, and Gemini. Prompt length × model × retry × call volume → cost per idea and per.
- Playgrounds Structured Schema Validator for Finance
Paste LLM JSON output and validate against four pre-built finance schemas — research output, trade decision, risk snapshot, peer comparison — with sanity.
- Playgrounds Hallucination Detector
Paste a source document + an LLM's extraction. Every numeric claim in the output is checked against the source. Client-side. Catches silent fabrication.
Questions people ask next
Why does beam search sometimes produce worse text than greedy?
Beam search optimizes for the highest cumulative-probability sequence, but the most probable sequence is not always the best one. Models tend to assign high probability to safe, generic, and repetitive continuations, so aggressively maximizing probability can yield bland or looping output, a phenomenon known as beam-search degeneration. For short structured extraction this rarely bites because the target is unambiguous, but for open-ended generation it is a real failure mode and is one reason sampling-based methods are often preferred for creative text.
Is greedy decoding the same as temperature zero?
Effectively yes for practical purposes: setting the sampling temperature to zero makes the model always choose the highest-probability token, which is exactly greedy decoding. This is why temperature zero is the standard setting for deterministic extraction. It is distinct from beam search, which also is deterministic but searches over multiple sequences rather than committing to the single most likely token at each step, and distinct from positive-temperature sampling, which introduces randomness on purpose.
Should I use beam search for JSON extraction?
Usually not. For short structured outputs the greedy path is almost always correct, so the extra compute of beam search rarely changes the result, and its bias toward bland sequences offers no benefit here. Reliability for structured extraction comes far more from schema enforcement, validation, and good prompting than from the decoding search strategy. Use greedy at temperature zero for deterministic, auditable JSON, and invest the effort in structured-output enforcement rather than in the decoder.
When should I use beam search instead of greedy decoding?
Reach for beam search when the globally most probable sequence matters and one early greedy misstep would derail the rest of the output, which is the situation in constrained generation, some translation, and tasks with a strict target form. Greedy commits to the best token at each step with no recovery, while beam search keeps several partial sequences and picks the best overall, so it can step around a locally tempting but globally worse choice. The cost is that beam search runs several forward paths per step, scaling with the beam width, and tends toward bland output. For the short structured extraction common in finance work, that tradeoff rarely pays off, so greedy at temperature zero stays the default and beam search is the exception you justify case by case.
Sources
- The Curious Case of Neural Text Degeneration , Holtzman et al., ICLR (2020)
- Speech and Language Processing , Jurafsky and Martin (3rd ed. draft)
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