What are different decoding strategies for picking output tokens

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Table of Contents

• Traditional Decoding Strategies

  • Greedy Decoding

  • Beam Search

  • Top-K Sampling

  • Top-P Nucleus Sampling

• Emerging Decoding Strategies

  • Contrastive Search

  • Mixture of Decoding Methods

  • Speculative Decoding

  • Dynamic Temperature Scaling

• Computational Efficiency vs. Quality Trade-offs

Traditional Decoding Strategies

Greedy Decoding

– This is the simplest strategy: at each step, select the single most probable next token and append it to the output (Decoding Strategies: How LLMs Choose The Next Word). Greedy decoding is extremely efficient (only one forward-pass step per token) and uses minimal memory since it tracks only one sequence. However, it often produces sub-optimal text. By always taking the locally most likely token, it can miss better long-range outcomes . In practice, greedy outputs tend to be generic or overly repetitive (a form of neural text degeneration), lacking diversity and sometimes looping on phrases (Generating Human-level Text with Contrastive Search in Transformers). This makes greedy decoding less suitable for open-ended or creative generation, though it may be acceptable for tasks where a single highly probable answer is desired (e.g. straightforward QA) or when speed is paramount.

Beam Search

– Beam search generalizes greedy decoding by keeping multiple hypotheses at each time step (Decoding Strategies: How LLMs Choose The Next Word). It maintains a beam of K candidate sequences, expanding each by one token and then retaining the top K overall (by total probability) as the new beam . By exploring multiple branches, beam search can find higher-likelihood completions that greedy might overlook . Larger beam widths usually improve the chance of better (more probable) outputs, at the cost of more computation and memory (roughly proportional to K). Beam search is widely used in tasks like machine translation and summarization, where finding a coherent high-probability sequence is important. However, in open-ended generation, beam search often over-optimizes for likelihood, leading to repetitive or unnatural outputs (degeneration) similar to greedy decoding . Studies have found that beam outputs, while high-probability, lack the variation of human text, often repeating phrases and using a narrow vocabulary . In practice, moderate beam sizes (e.g. 3–5) can balance quality and speed, but very large beams tend to sacrifice diversity. Beam search is less common for creative text generation, but remains useful when determinism and coherence are prioritized over randomness.

Top-K Sampling

– Top-K sampling is a stochastic decoding method that injects controlled randomness. Instead of always picking the top token, the model at each step considers only the K highest-probability tokens and samples the next token from that subset (according to their relative probabilities) (Decoding Strategies: How LLMs Choose The Next Word). By limiting the choice to the top K options, this method avoids the long tail of low-probability tokens, which helps maintain coherence while still allowing some creativity (Decoding Methods Compared: Top-K and Other Token Selection Techniques). Developers often use top-K to prevent the model from picking extremely unlikely (potentially gibberish) tokens, thus improving output quality over pure random sampling. The parameter K directly controls the randomness: a small K (e.g. 5 or 10) makes the output closer to greedy (more predictable), while a large K yields more varied text. The downside is that a fixed K may not suit all contexts . If the model’s next-token distribution is very flat (many equally likely options), a small K could cut off legitimate continuations and make text dull or repetitive . Conversely, if the distribution is very peaked on a few tokens, a large K might include some irrelevant tokens or needlessly increase randomness . In practice, tuning K is important: values in the tens or low hundreds are common for large LLMs. Top-K sampling is relatively efficient (only requires sorting tokens by probability each step) and is popular for real-time applications that need some randomness (e.g. conversational agents), since it’s faster and simpler than beam search while often yielding more engaging text .

Top-P (Nucleus) Sampling

– Nucleus sampling is a variant of sampling that chooses from a dynamic subset of tokens based on probability mass rather than a fixed count (Decoding Methods Compared: Top-K and Other Token Selection Techniques). At each step, it includes the smallest set of top tokens whose cumulative probability exceeds a threshold P (e.g. 0.9), and then samples the next token from that set (Decoding Strategies: How LLMs Choose The Next Word). In other words, top-p adapts the number of candidates according to the model’s confidence: if the distribution is uncertain (spread-out probabilities), the nucleus will be larger, and if the model is confident (one or few tokens dominate), the nucleus will be small. This adaptive nature gives top-p sampling important advantages over top-K. It automatically broadens the sample pool for flat distributions, preserving diversity in situations where many continuations are plausible . And for peaky distributions, it narrows the pool to highly likely tokens, avoiding the inclusion of low-probability outliers . The result is a more context-sensitive balance between coherence and surprise. Top-p is widely used in modern LLM-based chatbots and creative generators because it often produces more natural, coherent text than top-K for a given level of randomness . Like top-K, it’s a one-pass sampling method (computationally similar to greedy aside from sorting and cumulative sum operations), so it remains efficient for deployment. Typically P is set around 0.9–0.95 in many applications: this retains the top 90–95% of probability mass, which experiments found yields fluent yet not deterministic outputs. Top-p can be combined with a temperature parameter to further tune the entropy of the sampling distribution, giving fine control over output style.

Emerging Decoding Strategies

Contrastive Search

– Contrastive search is a recently introduced decoding approach (2022) that aims to overcome the common pitfalls of both greedy and sampling methods by balancing fluency and diversity in a single objective (Generating Human-level Text with Contrastive Search in Transformers). The idea is to have the model consider two factors when choosing a token: (1) the token’s probability (model confidence) given the context, and (2) a degeneration penalty that discourages tokens too similar to those already in the context . In practice, contrastive search generates candidates from the top-K tokens (like a limited shortlist, e.g. K=4) and then selects the candidate that maximizes [probability – α * similarity] . Here the similarity term measures how alike the candidate token’s representation is to any previous token’s representation (e.g. using cosine similarity in the embedding or hidden state space) . A higher penalty (controlled by α) means the model will avoid choosing tokens that would make the output more repetitive or stale. When α=0, this method reduces to greedy search, so α trades off diversity against the model’s preference . In effect, contrastive search dynamically penalizes repetition and bland continuations while still following the model’s likelihood guidance. This often yields text that is both coherent (high probability) and much less repetitive or boring (Creating Human-like Text with Contrastive Search and GPT-2). Studies have shown contrastive search can produce near human-level quality on various generation tasks, significantly outperforming previous decoding methods in maintaining context and avoiding nonsense . The practical implications are that with contrastive search, even off-the-shelf LMs can generate more meaningful, self-consistent long texts without additional training . The computation overhead is modest: the model needs to compute similarities with the recent context for each top candidate token. This is a small cost (vector dot-products) compared to a full model forward pass, so contrastive search remains feasible for deployment. It’s increasingly popular in research prototypes and may start appearing in user-facing LLM applications that demand high-quality long-form output.

Mixture of Decoding Methods

– Instead of sticking to one decoding algorithm, researchers and engineers have experimented with hybrid approaches that combine multiple strategies to capitalize on their respective strengths. For example, one might run a beam search to generate a set of high-probability candidate completions, then sample among those candidates to introduce randomness and avoid the deterministic bias of beam search. Another approach is to alternate strategies at different generation stages – e.g. use greedy decoding or low-temperature sampling when the model is confident and switch to nucleus sampling in moments of uncertainty. Recent research even explores using different models or systems in tandem: one paper proposes a mixture-of-experts decoding, where several LLM agents vote on or take turns generating tokens, and the decoder picks the best token from among them at each step (Collab: Controlled Decoding using Mixture of Agents for LLM Alignment | OpenReview). The general idea is that a combination can offset the weaknesses of any single method. In practice, such mixtures can be complex to implement and slower, since you may be doing extra computation (like maintaining beams or multiple model passes in parallel). However, they can yield higher-quality results in challenging tasks. An example of a simple hybrid strategy in use is applying heuristic rules on top of sampling – e.g. first do nucleus sampling, but if the model starts repeating an n-gram, switch to a different strategy or reset the sampling parameters. Another example is diverse beam search, which modifies beam search to ensure the beams are dissimilar (introducing diversity) rather than all converging to the same high-probability sequence. These mixed approaches are still emerging, without a one-size-fits-all recipe, but they underline a key point: sometimes “a hybrid approach combining multiple techniques can yield the best results.” (Decoding Methods Compared: Top-K and Other Token Selection Techniques). Experimentation is often required to find the right blend for a given application. Due to their complexity, mixtures of methods are more likely to be found in advanced research or specialized systems (like controllable text generation) rather than standard public APIs, but they represent a promising direction for optimizing output quality.

Speculative Decoding

– Speculative decoding is a novel strategy aimed at speeding up LLM inference without changing the output. Introduced in 2022 and now being deployed in practice (e.g. by OpenAI and others), it uses a clever two-model setup to generate multiple tokens per iteration instead of one. The process works by pairing a large target model (the one whose output we ultimately want) with a faster assistant model (Speculative Decoding for 2x Faster Whisper Inference). First, the assistant model quickly “speculates” a batch of, say, N next tokens by sampling from the large model’s distribution (the assistant is trained or chosen to approximate the larger model) . Then the large model is invoked to verify those tokens in one go: the N tokens are fed into the big model to see what it would predict next . Any tokens from the assistant that match the large model’s prediction are accepted, and for the first token that doesn’t match, the process falls back to the large model’s choice (discarding the rest of the assistant’s batch beyond that point) . The accepted tokens are added to the output, and the procedure repeats for the next chunk. This way, many tokens can be generated per one large-model forward pass. Crucially, the large model’s verification step guarantees that the final output distribution is exactly the same as it would have been with the standard method (Looking back at speculative decoding) – so quality is not compromised at all. In fact, speculative decoding provably yields identical output to the normal greedy/sample decoding that the large model would produce, just faster . The trade-off is the overhead of running the smaller model, but since that model is much faster, the net effect is a gain in throughput. For instance, experiments have shown 2–3× speed-ups in generation with no loss in accuracy . This is extremely valuable for real-time applications and serving high loads, as it reduces latency and can cut computational costs (fewer expensive GPU cycles per token). Speculative decoding does require maintaining an extra model in memory (the assistant) and some bookkeeping to handle mismatches, but frameworks are being developed to support it easily. Overall, speculative decoding is an exciting emerging technique that many see as a “free lunch” for inference: faster generation without the usual quality vs. speed trade-off.

Dynamic Temperature Scaling

– Dynamic temperature decoding adjusts the softmax temperature parameter on the fly during generation, instead of using a fixed temperature for the whole sequence. The temperature controls how “sharp” or “random” the model’s output distribution is (low temperature => more deterministic, high => more random). In traditional usage, temperature is a fixed hyperparameter, but recent research suggests that dynamically adapting it at each time step can yield better consistency (EDT: Improving Large Language Models’ Generation by Entropy-based Dynamic Temperature Sampling) . The intuition is that a model’s confidence in the next token can vary throughout a passage: sometimes the context clearly suggests a next word (high confidence, low entropy distribution), and other times multiple continuations are equally plausible (low confidence, high entropy). With dynamic scaling, the decoder can respond to these changes – for example, lowering the temperature when the model is unsure to avoid incoherent leaps, or raising the temperature if the model is over-confident to prevent boring, repetitive continuations. One approach (Entropy-Based Dynamic Temperature, 2023) computes the entropy of the model’s next-token distribution at each step and uses it to set the temperature for sampling . This way, the amount of randomness is tied to the model’s uncertainty: tokens predicted with high uncertainty lead to a lower temperature (sharper focus), ensuring the choice still fits the context, whereas very predictable tokens might use a slightly higher temperature to inject some variation if needed. Other formulations use measures like KL divergence or probability gaps to adjust temperature. The net effect is a more context-sensitive decoding that can improve coherence and preserve diversity compared to a fixed-temperature scheme . In evaluations, dynamic temperature methods have shown improved quality on tasks like story generation, question answering, and code generation, achieving a better balance between fluent output and originality than any single static setting . Computationally, this technique is lightweight – it’s just an additional calculation per token (no extra model passes). It can be combined with any sampling-based strategy (e.g. top-p or contrastive search) as a modulation mechanism. Dynamic temperature scaling is still an emerging idea, so it’s not yet standard in most LLM APIs, but it’s gaining attention as users seek more reliable long-form generation. In practice, it could be very useful for lengthy, coherent outputs: as the model generates more text, it can adjust its “creativity dial” to stay on track and avoid both dullness and derailment.

Computational Efficiency vs. Quality Trade-offs

Different decoding strategies come with varying costs and output characteristics. Choosing one involves balancing speed, resource usage, and the desired quality of text:

• Greedy Decoding: Efficiency: Very fast – only one candidate evaluated per step. It has the lowest computation and memory footprint (no branching) making it ideal for low-latency requirements (Decoding Methods Compared: Top-K and Other Token Selection Techniques). Quality: Often poor for open-ended prompts – outputs may be coherent but lack flair and can repeat phrases endlessly (Generating Human-level Text with Contrastive Search in Transformers). It’s best suited for scenarios where a single likely answer is acceptable or where deterministic output is needed. In real-time systems, greedy is sometimes used as a baseline due to its speed, but usually with the knowledge that quality might suffer.

• Beam Search: Efficiency: Slower than greedy by roughly a factor of K (beam width). It keeps multiple hypotheses, requiring more memory and computation at each step to grow and score beams. Large beams can significantly increase latency, so beam search is impractical for real-time use beyond small beam sizes. Quality: Excels at finding high-probability (often more grammatically correct or relevant) completions, which is why it’s favored in tasks like translation. However, for long-form or creative tasks it can produce bland, highly predictable text (Decoding Strategies: How LLMs Choose The Next Word). There’s a trade-off in beam size: a beam of 5–10 can improve grammaticality and recall of information, but beyond that returns diminish and text can become over-optimized and lose diversity. Beam search is thus optimal when you need a reliably coherent answer and can afford extra compute – for instance, offline content generation or multi-step processes (like generating multiple options then post-selecting one).

• Top-K Sampling: Efficiency: Similar to greedy in complexity – the main overhead is sorting the probability vector to find the top K, which is negligible for typical vocabulary sizes and small K. It generates one token per step like greedy. Quality: Offers a controllable randomness–coherence trade-off. With a well-chosen K, the output is more interesting and varied than greedy while mostly staying on-topic (Decoding Methods Compared: Top-K and Other Token Selection Techniques). If K is too low, the model might still repeat itself or get stuck on safe clichés; if K is too high, it might include odd choices. In practice, top-K is effective for moderately open-ended tasks and is used in many interactive applications (chatbots, dialogue systems) where some unpredictability is desired but wild errors must be limited. It’s a good middle-ground method when beam search is too slow and pure random sampling too incoherent.

• Top-P Sampling: Efficiency: Also very fast – requires computing a cumulative sum to determine the nucleus cutoff, which is trivial overhead. It retains the same one-token-at-a-time generation process. Quality: Generally superior flexibility across varying contexts. Nucleus sampling adapts to the confidence of the model, often yielding more coherent and contextually appropriate text than top-K, especially for large-scale LLMs (Decoding Strategies: How LLMs Choose The Next Word). Because it dynamically adjusts the candidate set, it tends to avoid the failure modes of top-K in extreme cases (e.g. it won’t accidentally truncate all options in a flat distribution). Top-p is widely regarded as a go-to for high-quality open-ended generation (it’s used in many state-of-the-art LLM deployments) because it strikes a good balance of fluency and creativity with minimal tuning. For real-time applications, top-p is usually as viable as top-K – both are lightweight. One must still choose the P threshold carefully: a very high P (~1.0) approaches pure sampling (risky), while too low P makes outputs deterministic. But in practice, values ~0.9–0.95 work well for a broad range of tasks, from chat dialogue to story writing, giving a nice trade-off where outputs are both sensible and not overly repetitive.

• Contrastive Search: Efficiency: Moderately higher cost per token than basic sampling. In addition to sampling from the top-K candidates, the model computes similarity scores between candidate tokens’ embeddings and the existing context. This involves dot products with the representations of prior tokens, which can add overhead if the context is long and K is not very small. For example, with K=4 and a context of 100 tokens, that’s 4×100 similarity computations per step – easily handled on modern hardware, but indeed more work than a simple argmax. Memory-wise, the model needs to store recent token representations to compare against. Despite this overhead, contrastive search can often be run nearly in real-time for reasonable sequence lengths, especially with optimized libraries. Quality: It offers a high-quality output – avoiding the common quality trade-off where more randomness risks incoherence. By actively suppressing repetitive or bland continuations, it produces text that humans judge as more engaging and coherent (Creating Human-like Text with Contrastive Search and GPT-2). This makes it attractive for applications like story generation, long dialogues, or any use case where maintaining the user’s interest and avoiding redundancy is crucial. In those settings, the slight speed cost is worth the improvement in text quality. However, if maximum speed is needed or the text is very short, simpler methods might suffice. Contrastive search is a cutting-edge strategy, so it’s primarily seen in research and some advanced products, but its strong quality makes it likely to become more common as optimization reduces its latency.

• Mixture Strategies: Efficiency: Combining methods usually increases computational cost. Running two decoders in parallel or sequentially (e.g. generating multiple candidates via different methods and then fusing or selecting) means extra forward passes or more complex logic. For instance, generating candidates with beam search and then sampling among them might double the work. Similarly, using an ensemble of models (mixture-of-experts decoding) multiplies cost by the number of models involved. Because of this, hybrid approaches are often only used offline or in specialized cases where quality requirements justify the cost. Quality: Potentially best-of-both-worlds if done properly. A hybrid decoder can achieve higher coherence than naive sampling and more diversity than pure beam search. It can also allow for dynamic decision-making – choosing the right tool for each context (as in the mixture-of-agents approach where each token is picked by the most appropriate expert model) (Collab: Controlled Decoding using Mixture of Agents for LLM Alignment | OpenReview). In practice, simple hybrids (like sampling from beam outputs) have been used to avoid failure cases of individual methods. More complex mixtures are still experimental but show promise in aligning outputs to desired traits (truthfulness, style, etc.). They are not typically needed for everyday deployments – fine-tuned models with nucleus or contrastive decoding are usually enough. But for pushing the limits of quality – especially in creative writing or when aligning AI output to multiple objectives – mixed strategies can offer incremental gains at the expense of more computation.

• Speculative Decoding: Efficiency: High throughput – this method is specifically designed to improve inference speed. In ideal conditions, it can achieve 2–3× faster token generation (Looking back at speculative decoding). The main computational cost is running the assistant (draft) model, but since that model is much smaller or simpler, its cost per token is low. Meanwhile, the large model is invoked less frequently (once per batch of N tokens instead of every token). The trade-off is that you are doing extra work that might be thrown away: if the assistant’s guesses diverge from the large model early, some of the guessed tokens won’t end up in the output (wasted compute). However, if the assistant is well-chosen to match the larger model’s predictions, the hit rate is high and yields big savings. Memory-wise, you need to host two models, which can be heavy (but often the assistant can be a quantized or distilled version of the main model to save space). Quality: No trade-off in quality – by design, speculative decoding gives exactly the same quality/output as the original decoding procedure of the large model . It does not alter the generation algorithm’s inherent choices; it’s purely an optimization. Therefore, it’s ideal when you want to speed up responses and keep the output identical to a trusted method (like greedy or nucleus sampling on the full model). Speculative decoding is well-suited for real-time applications and high-volume services – essentially anytime latency or cost is a concern. It’s already being adopted in LLM serving frameworks and APIs because it improves efficiency without needing to retrain models or compromise on response quality. The only downside is implementation complexity, but as libraries begin to support it, that downside is shrinking.

• Dynamic Temperature Scaling: Efficiency: Minimal impact on speed and memory. The algorithm only adds a calculation of a metric (such as entropy of the distribution) and a temperature update each step, which is negligible compared to the forward pass of an LLM. Thus, it can be used in real-time systems without issue. Quality: It tends to enhance coherence and stability of the generated text, especially for longer outputs. By adapting to the model’s confidence, dynamic temperature can prevent the model from veering off when uncertain and avoid monotonous rants when the model is very sure of itself. In effect, it smooths out the generation, reducing abrupt randomness spikes or overly deterministic stretches. This yields text that is contextually consistent – later parts of the text remain on topic and stylistically in line with earlier parts, since the decoding strategy adjusts to keep it so. At the same time, because temperature isn’t locked low the entire time, the model still has opportunities to be creative when it makes sense. Empirical results show that dynamic schemes achieve a better quality-diversity balance than any fixed temperature setting (EDT: Improving Large Language Models’ Generation by Entropy-based Dynamic Temperature Sampling). This is particularly useful in creative and open-ended tasks (storytelling, dialogue) where the context can evolve and the decoding needs to stay responsive to that. It may be less critical for short, factual completions (where a fixed low temperature might suffice), but for lengthier interactive sessions or narrative generation, dynamic temperature helps maintain user engagement with coherent yet lively responses. As an emerging technique, it’s a promising option to include in future LLM deployments aimed at high-quality, long-form generation.

In summary, there is no one-size-fits-all decoding strategy. Real-time applications (like live chat or interactive assistants) tend to favor methods that are fast and reasonably coherent: greedy or low-variability sampling (top-K or top-p with narrow settings) are common, potentially augmented by speculative decoding under the hood for speed-ups (Decoding Methods Compared: Top-K and Other Token Selection Techniques). These ensure the model responds quickly and sensibly, even if the wording is a bit predictable. On the other hand, creative or open-ended tasks benefit from strategies that inject more randomness and look ahead to avoid dullness – top-p sampling with a tuned temperature is widely used for this, and newer methods like contrastive search or dynamic temperature scaling can further improve the richness and consistency of the text. Those come with some computational overhead, which is usually acceptable in content-generation scenarios where quality matters more than a few hundred milliseconds of latency. Ultimately, developers often experiment with multiple strategies (and parameters) for their specific use case . Many production systems also incorporate safety or quality filters post-generation, which can influence the choice (for instance, a slightly more random method might be fine if you have a re-ranking stage to pick the best output among many). The landscape of decoding strategies continues to evolve, but having a solid grasp of these traditional and emerging techniques allows one to tailor LLM inference for the optimal balance of speed and output quality.