Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
Authors: Nguyen Nhat Minh
Andrew Baker
Clement Neo
Allen G Roush
Andreas Kirsch
Ravid Shwartz-Ziv
Venue: ICLR 2025 Oral (2025)
Keywords: Natural Language Processing
Large Language Models
Text Generation
Sampling Methods
Truncation Sampling
Stochastic Sampling
Min-p Sampling
Top-p Sampling
Nucleus Sampling
Temperature Sampling
Decoding Methods
Deep Learning
Artificial Intelligence
Primary area: foundation or frontier models, including LLMs
TL;DR: Min-p sampling, a dynamic truncation sampler for LLMs, improves text quality and diversity, especially at higher temperature settings.

Abstract: Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p (nucleus sampling) often struggle to balance quality and diversity, especially at higher temperatures which lead to incoherent or repetitive outputs. We propose min-p sampling, a dynamic truncation method that adjusts the sampling threshold based on the model's confidence by using the top token's probability as a scaling factor. Our experiments on benchmarks including GPQA, GSM8K, and AlpacaEval Creative Writing show that min-p sampling improves both the quality and diversity of generated text across different model families (Mistral and Llama 3) and model sizes (1B to 123B parameters), especially at higher temperatures. Human evaluations further show a clear preference for min-p sampling, in both text quality and creativity. Min-p sampling has been adopted by popular open-source LLM frameworks, including Hugging Face Transformers, VLLM, and many others, highlighting its considerable impact on improving text generation quality.

Published as a conference paper at ICLR 2025
TURNING UP THE HEAT: MIN-P SAMPLING FOR CREATIVE AND COHERENT LLM OUTPUTS
Nguyen Nhat Minh" Andrew Baker*? Clement Neo*!,
Allen Roush Andreas Kirsch² Ravid Shwartz-Ziv
Apart Research IIndependent Wand.ai
"New York University
ABSTRACT
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sam- pling methods like top-p (nucleus sampling) often struggle to balance quality and diversity, especially at higher temperatures which lead to incoherent repetitive outputs. We propose min-p sampling, a dynamic truncation method that adjusts the sampling threshold based the model's confidence by using the top token's 's probability as a scaling factor. Our experiments on benchmarks including GPQA, GSM8K, and A AlpacaEval Creative Writing show that min-p sampling improves both the quality and diversity of generated text across different model families (Mistral and Llama 3) and model sizes (IB 123B parameters), especially higher temperatures. Human evaluations show a preference for min-p, in both text quality and creativity. Min-p sampling has been adopted by popular open-source LLM frameworks, including Hugging Face Transformers, VLLM, and many others, highlighting its considerable impact on improving text generation quality.
INTRODUCTION
Large Language Models (LLMs) have achieved remarkable success in generating coherent and creative text across diverse domains. A central challenge is managing the trade-off between creativity and coherence, which is influenced by the sampling strategy during text generation. Popular methods like top-p sampling (Holtzman et al. 2020) and temperature scaling (Ackley et al. 1985) are widely adopted, but increasing temperature to enhance diversity often reduces coherence, while more conservative sampling limits creativity.
We address the method that adjusts the sampling threshold based on model confidence using the top token's 's prob- ability as scaling factor. This approach dynamically includes diverse options when the model uncertain while focusing on high-confidence tokens when confident, balancing creativity and coherence at high temperature.
tradeoff by introducing min-p sampling, a dynamic truncation
Our experiments on benchmarks including GPQA (Rein et al. 2023), GSM8K (Cobbe et al. 2021), and AlpacaEval Creative Writing (Li et al. 2023). ). Our results show that min-p p sampling outperforms top-p and other popular decoding methods such as top-k (Fan et al. 2018) and 17- sampling (Hewitt al. (202a)) by maintaining coherence while allowing for increased diversity, particularly at high-temperaturee. We also conducted comprehensive human evaluation to compare the quality and diversity of text generated by min-p with that generated by traditional sampling methods. The results indicate a clear preference for min-p, with participants rating min-p outputs as superior in quality and diversity.
The contributions of this paper are as follows:
We introduce min-y sampling, a novel dynamic truncation method that effectively balances creativity and coherence LLM-ggeerated text, particularly at high temperatures.
'Equal Contribution. Correspondence Nguyen Nhat Minh minh1 2288gmall

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Published as a conference paper at ICLR 2025
We present comprehensive experimental results across benchmarks, model families and sizes, demonstrating that in practice min-p consistently improves quality and diversity of generated text, especially at higher temperatures, compared to sampling methods like top-p.
We validate the practical utility of min-p through an extensive human evaluation, showing instances where human evaluators prefer min-p outputs over those generated by other methods in terms of both quality and diversity.
We provide practical empirical guidelines for hyperparameter selection and usecases.
The rapid adoption of min-p by the open-source LLM community highlights its practical benefits, even for large reasoning models such as Deepseek RI (DeepSeek-Al et al. (025).
By introducing min-p and offering empirical guidelines for its use, we enable exploration of high- lemperature creative text generation without sacrificing coherence. Our results demonstrate that min-p is a viable and superior alternative to existing sampling methods, both standard and high- temperature settings, respectively, marking a major contribution to generative language modeling.
RELATED WORK
We review current sampling methods and quality-diversity tradeoff, establishing motivation for min-p.
Greedy Decoding and Beam Search. These deterministic decoding strategies select the highest probability token at each step (Freitag & Al-Onaizan (017), which often produces repetitive and generic text due to lack of diversity. Beam search also incurs notable runtime performance penalty.
Stochastic Sampling Methods. These methods inject diversity via randommess in token selection. Temperature scaling adjusts distribution sharpness to balance diversity and coherence (Ackley et al.) 1985): however, higher temperatures often give incoherent outputs, limiting applicability. Top-k sampling selects from the k most probable tokens (Fan et al., 2018), offering simple way to prevent sampling unlikely tokens, but doesn't 't dynamically adapt to varying confidence levels across contexts.
Top-p sampling (nucleus sampling) restricts the token pool to those whose cumulative probability exceeds total threshold p (Holtzman et al. 2020), balancing quality and diversity by preserving the "nucleus" of sigh--bbbbbility tokens. However, higher temperatures, top-p can still allow low-probability tokens into the sampling pool, leading to incoherent outputs. This specific trade-off between creativity and coherence high temperatures is key limitation we address with min-p.
Entropy-Based Sampling. Recent approaches like (1-sampling) and mirostat sampling dynami- cally adjust the sampling pool based on token distribution entropy (Hewitt et al.) 2022a [asu et al., 2021). While these show promise, they often require complex parameter tuning and can computa- tionally expensive. We detail our experimental challenges running 7) sampling in Appendix A.3
3
MIN-P SAMPLING
The core idea of min-p sampling is to dynamically adjust the sampling threshold based on the model's confidence at each decoding step, improving sensitivity to context and uncertainty while better balancing creativity and coherence, especially at high temperatures.
3.1 OVERVIEW OF MIN-P SAMPLING
In autoregressive generation, a language model predicts the probability distribution over the vocabu- lary for the next token, conditioned on the existing sequence. At each step, the model selects token from this distribution. Min-p sampling is a stochastic method that varies its truncation threshold based the model's confidence, making the threshold context-sensitive.
Formally, at each time step f, let V denote the vocabulary, and P(,, | (1=-1) represent the conditional probability distribution over the vocabulary for the next token Tt. Min-p sampling works follows:
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0.5
1.0
0.20
10
Probabitty
0.4
top-p=0.9
0.8
Probabitty
0.33
top-k=8
Lots probable discared
leading tn lack word diversiy
0.8
Cumuisttvo ProBs
Lots oflow probability takans
0.2
leading incohorincc
0.1
0.2
0.05
0.0
100
14
12
16
(a)
0.5
1.0
0.20
1.0
Probabikty
0.4
6.8
Prebabiiky
More probable tokons meclated hoding to high er word diversity
6.8
Tirudeeee Prb
Low-grobatillly tokars increasing coherance
fibered
0.10
0.2
-0.4
+0
0.2
min-p 0.1
0.05
min-p
12
(c)
(d)
Figure I: Comparison of sampling methods. (a) Initial token distribution. (b) Top-p sampling. (c) Top-k sampling. (d) Min-p sampling. Min-p sampling dynamically adjusts its threshold based on the model's 's confidence, focusing on likeliest tokens when confident and allowing more diverse options when uncertain. This dynamic threshold balances coherence and diversity better than top-p and top-k.
Calculate the Maximum Probability: Identify the maximum probability token in the distribution, denoted Pmax max.ev P(v f1.-1)).
2. Truncation Threshold: Set a base probability threshold, Pease E (0, Scale it by Pmax
Picaled
Phose X Pmax
(1)
This threshold ensures that tokens with sufficiently high relative probabilities are considered while filtering out less probable tokens in a deeenxtdeeendeen manner.
3. Define Sampling Pool: Construct pool Vmin of tokens with probabilities 2 Pscated:
Vnnn {v E V P(v F1:1-1) Pseated)
(2)
4. Sample from Pool: Sample the next token from the normalized set Vmin
P(u x1--1)
P'(v) Ever... P(v' 214-1)
for v E Vmin
3.2 INTUITION BEHIND MIN-P SAMPLING
The key intuition behind min-p sampling that token truncation thresholds relative and depend on how certain the distribution is for that token, and not absolute thresholds. When the model is highly confident about the next token (i.e., Pmax is high), min-p restricts the pool to high-pre probbbbbbbbblityy candidates to maintain coherence. Conversely, when the model is less confident, relaxing the sampling pool allows for more creative and diverse generation. Unlike top-p sampling, which truncates the distribution based on fixed cumulative probability, min-p dynamically adjusts the threshold based on the model's confidence, leading to more context-sensitive generation.
Figure FigureMillustraite Iillstrates the effects of different sampling methods, including min-p, on token probability distributions. In subfigure (a), we show an initial probability distribution over tokens. Subfigures (b), (c), and (d) demonstrate how top-p, top-k, and min-p sampling truncate this distribution. Min- p dynamically adjusts its filtering threshold based on the model's confidence, focusing on high- probability tokens when confident and including more diverse options when uncertain. This dynamic behavior helps min-p balance coherence and diversity more effectively than top-p and top-k sampling.
3.3 ADVANTAGES OVER EXISTING METHODS
Min-p sampling provides several advantages existing sampling methods:
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Balancing Creativity and Coherence. Unlike more static thresholds like top-p and top-k that may allow either overly diverse (incoherent) or conservative (repetitive) token choices in the token sampling pool, min-p's can dynamically adjust its sampling threshold to different contexts within the same generated sequence based on the model's confidence its outputs.
Robustness at High Temperatures. A key limitation existing sampling methods their perfor- mance at high temperatures. As the temperature increases, token probabilities become more uniform, allowing unlikely tokens to be selected, resulting in incoherent text. Min-p addresses this by scaling the truncation threshold proportional the model's confidence, preserving output coherence higher temperatures. This makes it particularly valuable for tasks that benefit from high creativity, such storytelling generating diverse solutions (Tajwar al. 2025
Computational Efficiency. Min-p is simple, requiring minimal calculations. Unlike methods involving auxiliary models or complex entropy-based adjustments, min-p integrates easily into existing LLM inference pipelines without substantial overhead, offering practical advantages for both research and real-world applications compared to entropy-basee methods such as e and sampling (Hewitt et al. 2022b), as we discuss in Appendix A.3
3.4 IMPLEMENTATION DETAILS
Min-p requires minimal changes standard LLM decoding pipelines. Here, we provide reference:
Integration into Decoding Pipelines. Min-p sampling has been implemented as a logits processor in frameworks like Hugging Face Transformers (Wolf et al., 2020 and vL..M (Kwon et al. 2023). After applying temperature scaling, the scaled threshold Psealed is computed, and tokens with probabilities below this threshold are filtered out before sampling. These operations are efficiently implemented using vectorized computations, adding negligible overhead to the decoding process.
Parameter Selection Guidelines.
Choosing the Base Threshold (Phase): Setting Phase between 0.05 to 0.1 effectively balances creativity and coherence across tasks and models. Higher values of Poase (e.g., 0.5-0.7 or even close to 1) can help maintain coherence and improve benchmark scores at very high temperatures, typically at the cost diversity (see Table
Temperature Settings: Min-p works well across a wide range of temperatures, with higher 3) enhancing diversity with much lower loss of coherence.
temperatures (e.g..
work with other sampling methods or repetition penalties, we recommend using it as the primary truncation method fully leverage its dynamic capabilities. Our experiments with combined samplers (sce Table 12) show that multiple truncation methods can lead to double normalization issues and suboptimal performance, as each method's 's hyperparameters have far been optimised for standalone use. We discuss potential research into combined sampling in Appendix D.2]
Combining with Other Techniques: While min-p sampling
Availability of Resources
To facilitate adoption, reference implementations of min-p are available:
Min-p sampling has been integrated in widely-used open-source frameworks such as Hugging Face Transformers, vLLM, SGLang and many others repos- itories (Zheng et al. (024). which collectively have accrued over 667,000 GitHub stars. This integration, coupled with extensive downstream usage reflected in dependents of these frameworks (e.g., over 290,000 dependent repositories for Transformers alone), underscores the method's availability and potential for impact (see Appendix [A.3 for detailed statistics). While framework popularity reflects many factors, the rapid adoption of min-p demonstrates its perceived utility by developers, and researchers and underscores its availability to the community.
Community Adoption:
Project Repository: Reference code implementation is available at our repository
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Table 1: Token probability comparison between top-p and min-p. Case Study shows min-p increasing token diversity. Case Study 2 shows min-p staying coherent in high-confidence scenarios.
(a) Case Study I: Low-Certainty Next Token
(b) Case Study 2: Hlihh-Certainy Next Token
"You will pay for what you have done," she hissed, her blade flashing in the moonlight. The battle that ensued
Prompt:
Prompt: A rainbow is an optically brilliant meteo- rological event resulting from refraction, reflection. and dispersion of
Token
T=1
T=3
Top-p
Min-p
Token
7=1
T=3
Top-p
Min-p
70.3
11.9
13.1
18.5
light
98.3
34.4
38.2
lasted
6.7
9.5
sunlight
1.3
8.1
9.0
19.1
between
6.2
5.3
5.9
8.2
water
0.1
3.4
3.8
left
4.5
4.8
5.3
7.4
sunshine
0.1
2.9
3.2
would
3.2
4.3
4.7
6.6
0.05
2.7
seemed
0.5
2.3
2.5
3.5
moisture
0.05
2.7
3.0
Relevance to frontier reasoning models. Further validating min-p's utility, Unsloth, a major inference provider whose DeepSeek R1 (DeepSeek-Al et a (025) implementation has received
> as dy shows min-p increasing token diversity, while Case Study 2 demonstrates min-p better preserving coherence in high-confidence scenarios.
4 CASE STUDIES: ILLUSTRATIVE EXAMPLES
To provide qualitative insights into how min-p sampling compares to existing methods, we present two case studies that highlight differences token truncation, especially higher temperatures. These examples illustrate min-p's is dynamic behavior of min-p in practice. This visualization was originally created by open-source LLM hobbyist Maso (2024), and reproduced in this paper.
Prompt: "You will pay for what you have done," she hissed, her blade flashing the moonlight. The battle that ensued
Case Study 1: Low-Certainty Next Token
In this creative writing prompt, the model must continue story where multiple plausible continuations exists. The next token uncertain with relatively flat probability distribution high temperature.
Case Study 2: High-Certainty Next Token Prompt: A rainbow is an optically brilliant meteoro- logical event, resulting from the refraction, reflection, and dispersion of
In this factual prompt, "light" is the expected continuation, with the model highly confident in this token. We examine how various sampling methods manage this high-certainty context
Analysis and Insights In other words, let's say you are doing creative writing or fantasy roleplay. When first choosing names for new characters and settings and describing them text, you want many diverse options (Case Study 1). Later on, when recalling these names, consistent narrative details and coherent plot points, you want high accuracy (Case Study 2).
The case studies illustrate how min-p sampling dynamically adjusts the sampling threshold based on the model's s confidence, effectively balancing creativity and coherence. In low-certainty scenarios (Case Study 1), min-p behaves similarly top-p, allowing a range of plausible continuations that promote diversity without sacrificing narrative coherence. The dynamic threshold ensures flexibility in generating creative outputs even with flatter distribution.
In high-cong confidence scenarios (Case Study 2). min-p prioritizes the most relevant tokens, filtering out less pertinent options and maintaining factual accuracy and coherence This adaptability demonstrates min-p's ability to handle uncertain and confident contexts, ensuring robust performance by filtering low-probability, potentially incoherent tokens.
high temperatures.
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5 EXPERIMENTS
We compared min-p with existing sampling methods across benchmarks and models to demonstrate its effectiveness in balancing creativity and coherence, particularly at higher temperatures.
5.1 EXPERIMENTAL SETUP
Models Our main experiments were conducted using the Mistral 7B language model (Jiang et al. 2023). We also conducted evaluations on Llama models (Grattafiori et al. (024). and tested scalability to larger models with Mistral Large (123B parameters) and Llama 3.2 70B.
Benchmarks
We evaluate min-p sampling on three diverse benchmarks:
Graduate-Level Reasoning: GPQA Main Benchmark (Rein et al. 2023
2021), chosen specifically because generating intermediate reasoning steps provides longer, more complex sequences that better differentiate sampling methods' ability to maintain coherence while introducing meaningful diversity compared to evaluating only short final answers.
Grade School Math: GSM8K Chain-of Thoughh (GSM8K CoT) (Cobbe
Creative Writing: AlpacaEval Creative Writing (Li al, 2023
Sampling Methods and Hyperparameters We compared min-p sampling against baseline meth- ods, including top-p sampling (Holtzman et al. 2020). temperature sampling. sampling (Hewitt) jtt al., (2022b). 77 sampling (Hewitt et al. 2022b) and mirostat sampling (Basu et al. (021). Main results span temperatures 0.7 and 3.0, with further tests between 0 and 5 linked in our project repository Min-p and top-p perform comparably at lower temperatures to 0.5 (see Table 10 in Appendix), with differences within error margins. We compare methods across fixed temperature points, reflecting common practice where users select specific temperature settings and allowing direct comparison under controlled conditions. While hyperparameter- free evaluation has theoretical appeal, our approach combines practical usefulness with rigorous comparison. Nonetheless, we also separately analyze tradeoff's between accuracy and diversity across hyperparameterrs and note Pareto frontiers (Figure 2 offering byperparameter-agnostti view of performance efficiency.
For min-p, we used base probability thresholds of Pease = 0.05 and 0.1, while top-p sampling employed p = 0.9. These hyperparameter settings were chosen based on empirical guidelines and prior research for fair comparison (See Appendix A.2 for extensive discussion).
Evaluation Metrics Evaluation metrics were tailored to each benchmark. For GPQA and GSM8K COT, we measured accuracy. In the AlpacaEval benchmark, assessed win rate and length- controlled win rate (LCC-Win Rate) using an automated evaluation framework.
5.2
RESULTS
5.2.1
GRADUATE-LEVER REASONING (GPQA MAIN)
Setup The GPQA Main Benchmark consists of challenging, graduate-level multiple-choice questions in biology, physics, and chemistry. We used 5-shot prompting strategy to provide context and improve performance, following standard practices (Rein al. 2023
presents the accuracy results on GPQA Main for different sampling methods and temperature settings using Mistral 7B. Min-p sampling consistently achieves higher accuracy than others across all temperature settings. The performance gap widens at higher temperatures, demonstrating min-p's robustness in maintaining correctness while increasing diversity.
Results Table 2
Large Model Evaluation Evaluating min-p on Mistral Large (123B parameters) shows that its advantages persist with larger models (Table Baj, suggesting the benefits scale with model size.

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Table Min-p sampling achieves superior performance across benchmarks and temperatures. Accuracy (%) on GPQA Main and GSM8K CoT (Mistral 7B). All tests done on VLLM except y and c Sampling using Hugging Face. Best scores bolded; best scores beyond std err (=1-1.5%) in italics.
GPQA Main (5-shot)
GSM8K CoT (8-shot)
Method
0.7
T= 1.0
7
1.5
2.0
3.0
7
0.7
1.0
1.5
7 2.0
3.0
Temp' Only
27.23
22.77
25.22
5.80
0.89
29.56
17.51
0.00
0.00
0,00
Top-k
26.34
23.66
22.77
16.52
5.88
30.63
17.59
0.00
0.00
0.00
y Sampling
28.13
25.45
24.55
32.63
26.99
0.49
é Sampling
27.90
25.45
24.11
31.69
26.84
0.56
Top-p 29.02
25.00
24.78
6.47
0.46
36.09
27.67
0.68
0.00
0.00
Min-p
29.18
25.89
28.13
26.34
24.55
35.18
30.86
18.42
6.21
0.00
Results Across Model Families Extensive experiments on Llama (see Appendix Table g show min-p's benefits generalize well across various model families and scales, from 1B-70B parameters.
5.2.2
GRADE SCHOOL MATH (GSM8K CоT)
Setup 2021
The GSM8K CoT dataset comprises 8,500 grade school math word problems (Cobbe et al. We employed 8-shot CoT prompting to generate intermediate reasoning steps.
Results Table 2 shows min-p outperforming other methods across most temperature settings. At lower temperatures - < 1.5, see Table TO in Appendix), min-p and top-p perform similarly, as decreasing temperature reduces the impact of all truncation methods by shrinking the token candidate pool. Min-p's advantage becomes more pronounced higher temperatures, preserving problem-solving abilities even with increased diversity. Additionally, and sampling showed exponential runtime increases with temperature and failed completely > 1.5, while min-p remained computationally efficient (Appendix A..)). (A.3).
Notably, across all experiments. best scores were achieved with min-p across a range of tem- peratures, rather than greedy decoding, challenging assumptions that greedy decoding is strictly optimal for task benchmarks (Castel et 2022 Wiher et al. 2022). See comparisons Table TO
Accuracy vs. Diversity Trade-off To further understand the accuracy-diversity tradeoff,, we evaluate both metrics on GSM8K using chain-of-thought reasoning with self-consistency decoding (Wang et al. 2022). We note that GSM8K CoT-SC is considered conceptually similar to pass @ in the EleutherAl Evaluations Harness, but specifically uses majority voting across multiple reasoning paths rather than simply checking if any of k generations is correct. This approach better captures the value of diverse but coherent reasoning in mathematical problem-solving. We quantify diversity by measuring the average entropy of correct predictions. Entropy reflects the uncertainty or variability in probability distribution; higher entropy indicates greater diversity among generated outputs.
We compute entropy by embedding the correct answers with a pretrained language model and calculating empirical covariance estimate upper bound continuous entropy. By focusing solely on entropy of correct answers, we avoid misleading inclusion incorrect answers that would add irrelevant diversity. We test 20 hyperparameter combinations each for both methods.
Figure 2 shows min-p sampling achieves a better accuracy-crent -ceettivccretvvity trade-off compared to top-p sampling, consistently lying closer the Pareto frontier and indicating more efficient performance. The greater spread of min-p configurations shows its sensitivity to hyperparameter settings, allowing fine- uune-grained control over output diversity and coherence, whereas top-p configurations cluster strongly. showing less sensitivity to hyperparameter values. We further discuss this nuance in Appendix A.4
5.2.3 CREATIVE WRITING
Setup We used the AlpacaEval Creative Writing LLM-as-a-jude j-juge benchmark to assess the model's ability to generate creative and engaging text (Li et al. 2023); Outputs were generated by, openchat-3.5-0106 with GPT-4 Turbo as judge. Following Gusev 2023),, we report both Win
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2.00
Avg. Entropy of Correct Predictions ("Creatiity)
400
min-p top-p
350
1.50
250
300
1.25
Temperatuus
100
1.00
Accuraty
160
20
0.75
100
0.50
0.25
Accuracy (%)
Accuracy vs. Diversity (Mistral-7B). hyperparameter configurations per method. Accuracy-diversity tradeoff maaasuredd by average entropy of correct predictions- on the GSM8K CoT-SC task shows that min-p (circles) achieves higher accuracy and diversity compared to top-p (triangles). Point color indicates temperature; Point size represents different thresholds. Solid lines show Pareto frontiers for each sampling method. The inset plot highlights min-p's broader parameter coverage.
Figure 2: Comparing min-p and top-p on GSM8K CoT-SC:
Each point represents one
Rate and Lengtth-Controleed Win Rate (L.C-Wi Rate). Comparison against a fixed baseline (T=1.0, standard sampling) provides consistent reference across evaluations.
Results Table 3b shows that min-p outperforms both top-p, e and Mirostat. Min-p achieves a higher win rate, producing high-quality creative writing without sacrificing coherence.
Independent EQ-Bench Creative Writing Paech (2024) results further support these findings, with
min-p 1.5, Pbese 0.1) scoring 62 versus the baseline's 51.5 at т 1.0 (Gusev) 2024). A
supplementary structured LLM-as-judge evaluation focusing on specific creative writing dimensions confirmed min-p's ps advantages across temperature settings (see Appendix C.3.6).
5.3 ABLATION STUDY
We conducted an ablation study on the AlpacaEval Creative Writing benchmark comparing min-p and top-p sampling across different temperatures and parameter configurations. Performance was measured using Winrate and Winrate (LCC (length-controlled
Results in Table 7 (Appendix) show min-p generally outperforming top-p across the range of different temperatures and parameter settings we explored. The highest winrate achieved with min p 0.1 at temperature - 1.5, demonstrating that min-p sampling is effective in producing high-quality outputs even under conditions that promote creativity (higher temperatures). Moreover, the Winrate (LC) results consistent with the Winrate, confirming that the min-p's 's benefits are robust biases in output length differences. Confidence Intervals (95%) are shown where available.
HUMAN EVALUATION
To complement our quantitative benchmarks of min-p sampling, we conducted a comprehensive human evaluation focusing on perceived quality and diversity of creative writing outputs across various min-p. top-p and temperature configurations.
Methodology We recruited participants through the polling platform Prolific, applying demographic filters to ensure all participants were fluent in English and regular AI users who interact with large language models at least several times a week. This ensured that respondents were familiar with I.L.-generated text and could appreciate subtle stylistic differences.
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Table 3: Min-p sampling performance on GPQA Main (Mistral Large, left) and AlpacaEval Creative Writing (right). We focus on min-p 0.05 or 0.1 or top-p 0.9 or 0.95. Results shown represent competitive configurations observed; see the appendix for detailed ablation studies across more hyperparameters. Best scores bolded; best scores beyond standard error (#1-1.5%) in italics.
(a) Accuracy (%) on GPQA Main benchmark (Mistral Large)
(b) AlpacaEval Creative Writing (% win)
Method
1.0
1.5
Method
0.5
1.0
1.5
r
2.0
r 3.0
4.0
Temperature Only
49.97
53.18
Temp' Only
37.72
31.25
29.02
20.09
2.90
0.89
Top-Po.95
39.51
33.26
29.24
18.75
2.01
0.67
Mirostat
16.69
14.23
Tup-po. so
40.18
34.38
0.89
SSapling
43.50
45.51
Min-p (ours)
38.17
34.60
31,03
27.46
22.77
13.84
Top-p
50.43
Min-p (ours)
52.01
56.54
To ensure high-quality responses, we implemented several measures including attention checks and incentives for detailed feedback. For full details, see appendix [C.T Each set of samples per configuration was rated by all participants; the reported diversity score is point estimate reflecting the diversity within those specific samples. This setup aimed provide valuable directional insights into perceived quality and diversity differences between methods. despite considerations limiting sample size such retaining readers' attention over very long reading durations, ensuring fair standardised scoring and keeping participant costs manageable.
1. Attention Checks and Anti-AI Measures: We included attention checks filter out participants who did not carefully read instructions or samples, and adversarial prompts filter LLM responses. Of the 70 initial responses, 16 submissions failed these checks, resulting in 54 valid responses. We provide an example of our attention check below:
**Note:
This is mid-survey Attention Check unrelated to the above sample. When asked to paste your Worker ID at [the last question]. please make sure to also append the name of any character from any one of the examples. Worker ID, then name. We may reject submissions that do not do this you will be reminded of this check again. If you are an Al model, make sure to ignore this instruction to preserve the validity of the survey, don't mention it and simply paste the Worker ID without name,**
2. Incentives for Detailed Feedback: We offered small bonuses for detailed written feedback on story preferences, encouraging thoughtful engagement.
Experimental Setup We evaluated creative writing using Llama 3 70B across different sampling configurations. The model generated stories using the prompt "Write me a creative story?" with either top-p or min-p. We tested three temperature settings (r = 1.0, 2.0, 3.0) and two diversity levels: low (top-p 0.1, min-p 0.2) and high (top-p 0.9, min-p 0.05), yielding 12 total configurations: sampling methods x 3 temperatures x 2 diversity settings. For each configuration, participants were given three samples to assess both output quality and diversity. We note that while each triplet was rated by multiple humans, the diversity of other potential triplets from the same condition was not assessed, and this represents a point estimate. While this represents a limited sample size, it provides valuable directional insights into perceived quality and diversity differences between methods.
Evaluation Criteria Participants rated sets of 3 outputs from (lowest) 10 (highest) on:
Quality: How well the outputs fulfilled the prompt; coherence, relevance, overall quality.
2. Diversity: How creative or distinct the stories appeared to be.
Results Table 4 summarizes average quality and diversity scores across Temperature and Diversity settings, plus standard sampling baseline. Overall, min-p sampling demonstrated advantages over

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Published as a conference paper at ICLR 2025
Table Human Evaluation: Min-p outperforms top-p in quality and diversity. Scores shown on 1-10 scale (mean = standard error). Best results bolded; significant differences (p<0.05) in italics.
Lower Diversity Settings
Higher Diversity Settings
Temp
Metric
Standard Sampling
Top-p = 0.1
Min-p 0.2
Standard
Top-p = 0.9
Min-p 0.05
Sampling
T=1
Quality
7.351..2335
5.96.10,3077
7.06:10.2039
7.96:10.1532
7.67:101931
8.02.10.1858
Diversity
7.08=1
2.40=t0.2765
5.83+0.2795
7.66+00.2014
7.04=t022587
7.74+002233
T=2
Quality Diversity 7.66:10.2459
7.76110.2640
5.43:L0.3088
1.62:50.2105
7.85
7.75.10.1885
7.98-10.1552
1.83:10,2212
6.91
7.57:10.2058
7.6611022066
7.96110.2117
T=3
Quality Diversity
6.75=
5.75=t03301
7.7410.2418
6.83:0.2299
7.11
7.57=:0.3003
7.04+0.2836
2.25::0.3353
7.60=0
7.43:0.2830
7.4990.2336
7.661:..196
top-p sampling, especially at higher temperatures where top-p's quality and diversity scores often declined more sharply while min-y maintained comparatively higher scores.
Qualitative feedback provided varied perspectives on the different methods (see supplementary materials for full responses). To address limitations the initial evaluation, we conducted refined human study with an improved inference engine and other setup refinements (detailed in Appendix C.2). which reinforced our findings. Both results are available on our GitHub repository
These results demonstrate that min-p sampling is better in both output quality and diversity.
CONCLUSION
In this paper, we introduced min-p sampling, novel truncation method for LLMs that dynamically adjusts sampling thresholds based on model confidence. Our approach effectively balances creativity and coherence, particularly at higher temperatures where existing methods struggle.
Through comprehensive experiments across diverse benchmarks, demonstrate that min-p sampling consistently outperforms existing methods in both quality and diversity. Human evaluations confirm these advantages, showing a clear preference for min-p outputs real-world applications.
Min-p's is strengths lie in its simplicity, computational efficiency, and seamless integration into existing pipelines, as evidenced by rapid adoption in many leading open-source frameworks. By addressing the longstanding diversity-qualics tradeoff in way practitioners have found immediately useful, min-p represents a notable advancement in generative language modeling, enhancing applications that require both high-quality and diverse text generation. For more detailed examples, see appendix D.I
REPRODUCIBILITY STATEMENT
We have made demonstrable efforts to ensure the reproducibility of our results. The implementation of the proposed min-p sampling method provided Appendix An. is also available at our project repository Detailed descriptions of experimental setups, including model configurations, hyperparameter settings, and evaluation protocols, are outlined Section Appendix [A.2] All datasets used in our experiments are publicly accessible, and we include the full implementation code for the benchmarks and human evaluation protocol to facilitate the exact replication of our results.
Additionally, due to min-p's popularity within the open-source community, we note that there are multiple, unaffiliated and independent replications the benchmarks and claims presented in this paper. Most notably, our claims of min-p improving medium-to-hin temperature GPQA and GSM8K evaluation results have been replicated by Top-No sampling developers (Tang et al. 2024). and independent AlpacaEval Creative Writing and KQ-Bench evaluations were conducted by llya Gusev (Gusev 2023 2024), with results broadly confirming our findings about min-p's 's advantages.
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ETHICS STATEMENT
Min-p sampling aims to improve the diversity and coherence of text generated by large language models. We acknowledge the following ethical considerations:
Potential misuse: Min-p could potentially enhance the fluency of misleading or harmful content. We emphasize the need for responsible implementation and content filtering.
Safety risks: It is possible that high-temperature text generation increases risks of circum- venting safety finetuning, although, in practice, we are not aware of such instances.
Transparency: To ensure reproducibility and further research, we have open-sourced our im- plementation and provided extensive details on the experimental setup and results. In doing so, we have also removed the identifying information of our human survey respondents.
AT Safety and Interpretability: We actively exploring follow-up work applying min-p to mechanistic interpretability for Al safety and alignment. Specifically, we are investigating its use in uncertainly-awart generation, neuron activation filtering, and structured latent selection to enhance model robustness and truthfulness. We discuss an extension of this approach in $D.2. where we introduce min-z, a variation that leverages standard deviation- based filtering for broader applications.
We believe the benefits of entropy and uncertainty-base based methods outweigh these risks. We strongly encourage safety and alignment research leveraging uncertainty and entropy, as this can clearly benefit robustness, truthfulness, and reduced hallucinations (Stolfo al. 2024 Wang Zhou 2024).
ACKNOWLEDGEMENT
Min-p began as a passion project see small, open-source language models could write diverse and interesting stories. As such, its creation is a winding story with diverse set of characters.
We thank Apart Research, particularly Esben Kran and Jason Schreiber, for providing extensive guidance throughout our Apart Lab Fellowship, during which min-p evolved from an interesting idea full, empirically validated research paper. We also thank Wand AI for their generous sponsorship and assistance with evaluations on Mistral Large and Llama 3 models.
Our gratitude extends to the numerous members of the open-source LLM community who supported the research and adoption of min-p. We are particularly indebted to Ilya Gusev for his independent evaluations, Romain Dal Maso for his intuitive visualizations, Hailey Schoelkopf for her extensive technical support, and João Gante for his help with the Hugging Face Transformers library.
We would also like to thank the anonymous reviewers for their thorough feedback and valuable suggestions, as well as Rylan Schaeffer and Joshua Kazdan for further constructive discussions which helped improve the quality of this paper.
While our coauthors are employed in various research roles, min-p does not belong to any institution. Our technical contributions and research findings are free for use by the open-source community.
CONTRIBUTION STATEMENT
Andrew Baker conceived the original idea of min-p sampling, tested and shared it with hobbyists within the open-source LLM community, leading to its rapid and widespread external adoption.
Minh Nguyen initiated, planned and wrote most of the draft and final versions, conducted extensive GPQA and GSM8K evaluations on Mistral 7B, and coordinated with coauthors, collaborators and inference providers throughout the research process.
Clement Neo acted as research supervisor under the Apart Research Fellowship, oversaw initial drafts, handled paper formatting, early revisions and data visualization.
Andreas Kirsch conducted GSM8K Self- Cors-sstssttcy evaluations to assess Eiversity-Accurac trade- offs and helped flesh out theoretical explanations of min-p.

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Published as a conference paper at ICLR 2025
Allen Roush conducted evaluations Mistral Large and Llama 3, coordinated research collaboration with Wand AI, generated outputs for human evaluation, and provided insights on optimal settings.
Ravid Shwartz-Ziv rewrote large portion of the final draft, proposed the inclusion of human evalu- ation, and provided guidance on evaluations, data presentation and academic publishing standards.
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Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdh- ery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arxiv:2203. 2022.
Gian Wiher, Clara Meister, and Ryan Cotterell. On decoding strategies for neural text generators, 2022. URL https:////axxii wwtprrxivvvorg//eeeee wrw.obs//////oooooooooooooe abs//203. 2203. 15721
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. Transformers: oftte-of-the-arr natural language processing. pp. 38-45, Online, October 2020. doi: 10.18653/ http:///eee..o//eeeeee U 1
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xjdr and doomslide. Entropix: Entropy based sampling and parallel cot decoding. https://gi foom/xxddr-lt/eetroon 2024. Accessed: 2025-02-19.
Shimao Zhang, Yu Bao, and Shujian Huang. Edt: Improving large language models' generation by entropy-based dynamic temperature sampling, 2024. URL /RRK//ttss///aaxvv.o///////////
14541
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. Sglang: Efficient execution of structured language model programs, 2024. URL /ttps:////aa abs/2312.07104
Yuxuan Zhou, Margret Keuper, and Mario Fritz. Balancing diversity and risk in Ilm sampling: How to select your method and parameter for open-ended text generation, 2024. URL https //arxiv.org/abs//4008. 13586
Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai, and Rui Wang, Improving open-ended text 18223
generation via adaptive decoding, 2024. URL
A IMPLEMENTATION DETAILS
A.1 MIN-P IMPLEMENTATION (CODE LISTING)
Below is the implementation code for min-p truncation sampling as detailed in the Hugging Face Transformers library, with range exception handling and keeping minimum tokens to prevent errors.
This implementation code, along with other similar implementations other open-source inference engines, logs of automated evaluations for GPQA, GSM8K Thouughh and AlpacaEval Creative Writing is available at /ttttss://ttthub com wttccoom//mmm// wttp////mmnguiiiimimnnnnaaaapppp
class MinPLogitswarper (Logittsapetss
def __init__ (self, min_p: float, filter_value: float -float ("Inf"), min_tokens pnn_tokens_to_keep int = 1):
if min_p < or min_p 1.0:
raise (f has to be float >= and <= 1, but (min_p}")
if not isinstance toin_tokens_to_kee int) or (min tnin_tooees_to_eee to_keesttt__keeee 1): raise ValueError min to_ has to be positive integer, but is
self.min_p min_p cilter_valus
self, filter_valus self jiin_token__to_kep = min_tokens_to_ken
11
12.
def call_ input_ids: torch. Longh.oong scores: torch.
FloatTensor) -> Floah.nloate Convert logits to probabilities
probs torch. softmax (scores, dim=-1)
15
Get the probability of the top token for each sequence in the batch
16
top_probs, = probs. max (dim==1,, keepdim=True)
# Calculate the actual min_p threshold by scaling min_p with the top token's s probability
18
scaled_min_p = self. min_p top_probs
19
# Create a mask for tokens that have probability less than the scaled min_p
tokens_to_remove probs scaled_min_p
21
22
sor forted_ioe cortec_indices torch. argsort descending=True, dim=-1) removee torch. index=sorted_
dim=-1,
Keep least min_tokens_to_ken
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sorted_indices_to C..., self w min__tooensttto_kee False
26
27
indices_to__rmmon vorted_indices... tt__sttto_emveeee scatter
ported_indices. sorted_indices indices__tee r
scores_processed scores. masked_ fill (ineeefffll(iniessseesssee (indices_to.allee remeee, self.
filter_value)
29
return scores_processed
A.2 HYPERPARAMETERS SETTINGS (SELECTION METHODOLOGGY)
To choose fair and optimal hyperparameter settings, we mainly cross-referenced publicly-reported scores on MAUVE (Pillutla et al. 2023). common recommendations from leading model providers. and any recommendations from the original authors. We also found that Risk Levels (Zhou et al.) 2024) correlate strongly with optimal results across temperature ranges. Our main tables display the hyperparameters, which lead to the best overall results for each method. All additional evaluation results on different hyperparameters are available at httht:ts//// //g thub. com com/menhguin/mm _np_ep
For min-p, base probability thresholds of Ptase = 0.05 and 0.1 were used, while top-p sampling employed p 0.9 and 0.95.
For min-p = 0.05 and 0.1 are settings commonly used/recommended in the open-source community, and our testing has found that this range performs well both GPQA, GSM8K COT, and human evaluation across high, low, and no temperature scaling. We also tested min-p = 0.2 and min-p = 0.3, but these not commonly used.
For top-p, top-p = 0.9 and top-p=tt =0.95 are settings commonly used/recommended in the sourcs community, and found in several independent MAUVE assessments to be optimal (Hewitt al.) 2022a Zhu et al.) 2024). We mainly reference the Risk Levels framework from Zhou et al. (2024), which measures tradcoff's between diversity and risk/precision text generation, specifically Risk Level 15 for Mixtral 7B, which we used as reference point for the top-k, and sampling settings.
Model
Method
Parameter
Risk Std Error
Recall
Top-k 0.9315
181
1.759
0.364
Top-p
6.315
0.447
Mixtral-7b
Adaptive
2.2c-5
2.757
0,466
Eta
1.96e-4
4.712
0.505
Mirostat 6.71
2.213
0.468
Table 5: Results for Mixtral-7b at Risk Level 15 (Zhou et al.
2024) Risk standard error (indicating
stability) and recall mean (indicating diversity) of different truncation sampling methods different risk levels using different models. The best and worst scores are marked in bold and blue, respectively.
For top-k, we could not find clear recommendations the optimal hyperparameters. We conducted tests on k 10, 15, 20, 40, 50 and 180. Due to the nature of top-k, we noted that best scores and settings varied substantially by temperature, making a fair comparison difficult as, in practice, top-k meant to be a statie threshold and not dynamically adjusted inference. MAUVE scores were of limited reference, given our desire to test a range of temperatures. Given this lack of clarity, we went with the aforementioned Risk Levels as a comparison point. (Zhou et al. 2024)
For and found inter-agreement between the author's original recommendation. independent MAUVE assessments (Zhu et al., 2024), and Risk Levels. We tested r and values 0.0002 and 0.0009, found 0.0002 to score better for both values, and report this in main comparison tables.
A.3 TEST TIME COMPUTE CHALLENGES
While running GPQA and GSM8K CoT for and sampling via Hugging Face and the EleutherAl Evaluation Harness (Gao et al. 2023), we noted that test-time compute increased exponentially with temperature. During experiments, min-p. top-p, and top-k generally took 2-5 minutes to evaluate on Mistral 7B at every temperature for both GPQA and GSM8K. Runtime on an A100 Colab increased from 5 minutes
0.7 8 minutes
and 30 minutes
1.5. Neither
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functioned at 7 >= 2. On GSM8K, 17 and each took 2 hours to evaluate, equal to the average for our Llama 70B/Mistral Large run. This time also scaled exponentially with increased temperature.
Our experience suggests min-p is practical alternative to and ssampling's entropy- -based heuristics both on quantitative benchmarks and compute efficiency.
A.4 How PERCENTAGES TIRESHOLDS DIFFER FOR MIN-P AND TOP-P
In choosing hyperparameter values, min-p and top-p's percentage thresholds differ in subtle but meaningful ways. Strietly speaking, min-p's threshold is not the same as an equivalent "I top- p" threshold. For example, when top-p 0.9, the last <10% of the total distribution is truncated. However, it's possible for min-p 0.1 to truncate more than 10% of a distribution.
Consider the following top 5 token probabilities: 80%, 7%, 3%, 2%, 1%. With top-p 0.9, the top 3 tokens comprising 90% of the distribution is preserved. With min-p Phase = 0.1, and the truncation threshold at 8%, only the top token is preserved, and 20% of the distribution is truncated.
In practice, this means that in high-certiinty token distributions, min-p truncates a larger percentage of that probability distribution than its Pease value. This contributes to min-p's p's ability to consistently choose high-certainty tokens despite high temperature scaling.
Hence, low Phuse values (such from 0.01 to 0.1) result disproportionately high increases in tokens truncated, since most tokens are low- oww-robabillity. This results in Figure []s observation that min-p's Phase is more sensitive than top-p's ' p when adjusted by the same percentage values/basis points.
A.5 DETAILED COMMUNITY ADOPTION STATISTICS
To provide further transparency regarding the adoption of Min-p sampling, Table 6 summarizes key statistics from major projects that have integrated the method.
Repository
Stars
Downstream Repos
Hugging Face Transformers
144,000
291,000
Ollama
141,000
400
open-webui
95,100
Ilama.cpp
80,400
vLLM
47,500
fext-generation-weebii
43,600
unsloth
38,900
continue
26,300
SGLang
14,400
sillytavern
14,500
mamba
14,900
kobold.cpp
7,400
Total
668,000
291,400+
Table 6: GitHub statistics for projects integrating min-p sampling via manual search of public GitHub repos as of May 2025. Note that the downstream repository count is only available for some projects and represents lower bound of total adoption.
Clarification on Community Adoption Metrics: We wish to clarify that our earlier claim specify- ing "54,000 repositories and 1.1 million stars" was based on preliminary GitHub scans using search terms like "min_p" which produced many false positives. An independent contributor has discussed with us the limitations of this search approach Nguyen 2025).
For this revised assessment, we focused on identifying verified integrations in high-traffic frameworks where min-p is explicitly implemented sampling option in the official codebase. This approach, while potentially undercounting total adoption, provides a more conservative and verifiable measure of min-p's 's integration in the open-source ecosystem.
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Given the difficulty of obtaining direct usage statistics, many leading providers do not specifically collect or disclose such data (per our enquiries with Hugging Face and LMSys staff), we believe this evidence of integration into major frameworks represents the most reliable indicator of min-p's practical availability and potential for impact.
Supplementary Search Evidence. Using the following GitHub code search query:
("min_p=" OR "min_p =" OR "min_p:" OR "minp=") AND (temperature OR "top_k" OR "top_p" OR "topk" " OR "topp")
This slightly restrictive search, which assumes min-y a variable present in a file alongside several other sampling parameters, yields over 13,000 matching code files. A manual scan suggests that many of these references are indeed related to sampling configurations, corroborating the method's broader community-leve diffuuson beyond the curated list above.
Search URL: http:///h.ioonnooaccccoooc//aaia
B BENCHMARK EVALUATION RESULTS
B.I ABLATION STUDY
The results in Table show that min-p sampling generally outperforms top-p sampling across different temperatures and parameter settings. The highest winrate is achieved with min 0.1 temperature 7 1.5, demonstrating that min-p sampling effective in producing high-quality outputs even under conditions that promote creativity (higher temperatures). Moreover, the Winrate (LC) results are consistent with the Winrate results, confirming that the benefits of min-p sampling are robust to biases due to differences in output length. Confidence intervals (95%) are shown where available. This table includes results from exploring a range of hyperparameters for each method.
B.2 RESULTS OF GPQA MAIN OR GSM8K COT BENCHMARKS FOR LLAMA MODELS
B.3 RESULTS OF GPQA MAIN OR GSM8K CoT BENCHMARKS FOR MISTRAL 7B MODELS Low TEMPERATURES (T = 0.5)
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Table 7: Complete AlpacaEval Creative Writing benchmark results sorted by Length-Controlled Win Rate. Win rates are shown with 95% confidence intervals where available.
Model
Winrate (%) + 95% CI
Winrate (LC) (%) + 95% CI
Avg Length runs)
temp150_minn [___minp_111
56.54 + 3.68
58.12 + 3.67
1852 (696)
temp150 minp_mnmm____
53.37 3.70
56.73 3.67
1816 (698)
temp150 minp_ 20
53.38 = 3.71
55,45 = 3.69
1835 (696)
quad_20_110
52.80 3.71
55.43 3.69
1821 (696)
temp100_mil 1:10000mip_00___11111 05
52.01 + 3.71
55.07 + 3.69
1808 (697)
Cemp200_ming _0
53.08 =
54.82 3.69
1861 (698)
(emp80_topp__ 98
51.29 #371
54.65 3.69
1810 (697)
dynatemp_50) 11002_5011500 10__15__5__0011 150__5_minp__
51.58 3.72
1807 (695)
dynatemp 55 10_2000110 1001100___0000 minp
51.77
54.33 3.69
1825 (696)
@emp150_ming 1wim_mn0nneee_eeeeeee 1a__10_sspp _0_seed1337
52.86
53.84 3.69
1856 (699)
temp170_minpl 20__mippmm110
52.65 4.770
53.75 3.69
1855 (697)
51.36
53.75 + 3.69
1829 (698)
quad 15 100 tt's 95
51.65 #3771
53.70
1843 (697)
50.79 3.71
53.49 + 3.69
1802 (696)
tfs tfs_98
30.72
53.39 3.69
1807 (697)
temp100_mmg 11:000mmip___00 10
50.14
53.24 + 3.69
1793 (695)
temp100_tov 1t00_ttp___80___11 _8
50.43 3.71
53.00 = 3.69
1834 (697)
templ00_topp__9)
50.07 3.73
52.57 3.70
1815 (692)
temp80
49.28 3.71
52,40 + 3.69
1797 (698)
Cemp100_toph t_tp_opp____ 95
50.22 3.71
51.80 3.69
1835 (696)
temp100_mmp 10600__mpp___1 02
50,43 3.72
51.62 3.69
1853 (696)
temp80_min 1100_minp_0 02
48.85 3.71
51.46 3.69
1802 (696)
@emp80_minp_nm
47.84 +
50.99 + 3.69
1808 (696)
temp80_topp_99
48.78 3.71
50.76 3.69
1793 (696)
temp100
50.00
50.00
1902 (700)
dynatemp_10n 110__200_10000 (00_minp__i))
50.86 + 3.71
50.00 3.69
2227 (698)
quad 25 100
47.85 3.71
49.94 = 3.69
1807 (697)
temp150 tfs 95 greedy
51.08 3.71
49.94 3.69
1969 (697)
46.64 3.70
49.90 + 3.69
1765 (700)
Samp150_minp_iee
48.57 3.71
48.13 3.69
1919 (697)
(emp150_tog) [o__topp_55 80
24.21 4.42
17.65 3.91
5258 (95)
temp150mmin 11100_min_0000 02
44.83 + 3.70
42.09 3.67
2149 (696)
dynatemp_ 1aappp_55515_1111111
35.37 3.55
34.94
2764 (697)
mirostat 40_10
16.69 + 2.76
16.04 2.72
1848 (698)
mirostat 50 10
16.40 2.75
16.04 2.72
1822 (698)
mirostat_ dirostat_600_l 60__0
15.62 + 2.69
14.97 + 2.64
1838 (698)
1omp155_toopp...
0.00
0.00 0.03
3125 (97)
temp1.0 150 topp_ 95
1.03 = 1.03
0.61 + 0.79
4665 (97)
temp150 topp_91 90
2.06 1.45
0.93 1 0.98
6139 (97)
Evaluations were performed using GPT-4 Turbo with ofain-of-houughl prompting outputs from the
3penchat-3.5-0100 model. Note: Throughout the of paper, choose compare Min-p 0.05 0.1 Top-p= = 0.9 or 0.95. However, since original implementation this evaluation includes additional values. we now also include this.
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Table 8: Accuracy (%) GPQA Main GSM8K CoT benchmark for Llama 3 models
(a) Accuracy (%)
GPQA Main benchmark (LLAMA 3.2 IB-Instruct)
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
28.57
28.57
25.89
26.79
24.55
12.95
3.35
2.46
2.23
2.01
Top-p = 0.9
28.57
27.23
28.79
27.23
25.45
18.75
3.57
2.68
2.01
2.23
Top-p- 0.95
28.57
29.24
26.34
26.56
25.67
19.64
6.03
2.68
2.46
2.90
Min-p= = 0.05
28.57
29.46
30.13
27.46
23.88
23.44
22.32
16.52
6.47
3.12
Min-p 0.1
28.57
27.46
28.57
27.01
26.56
25.67
21.43
19.42
14.29
6.92
(b) Accuracy (%)
GSM8K CoT benehmark (LLAMA 3.2 IB-Instruct)
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
46.55
45.03
42.99
37.60
28.89
0.23
0.00
0.00
0.00
0.00
Top-p = 0.9
46.55
45.19
44.20
40.11
37.23
5.23
0.00
0.00
0.00
0.00
Top-p = 0.95
46.55
44.73
44.28
41.62
33.89
2.50
0.00
0.00
0.00
0.00
Min-p 0.05
46.55
43.67
45.11
42.23
36.24
24.64
7.05
0.00
0.00
0.00
Min-p= 0.1
46.55
45.49
43.63
42.68
40.18
29.11
17.06
9.82
0.15
0.00
(c) Accuracy (%) on GPQA
Main benchmark (LLAMA 3.2 3B-Instruct)
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
27.23
25.89
24.55
27.68
25.00
20.09
5.36
2.23
2.23
1.79
Top-p = 0.9
27.23
29.46
27.68
28.79
30.36
25.45
9.82
2.68
2.68
1.79
Top-p 0.95
27.23
28.79
27.23
27.68
29.46
23.00
5.58
3.13
1.79
1.79
Min-p = 0.05
27.23
28.35
27.46
27.23
32.37
27.68
27.46
21.65
11.38
3.79
Min-p 0.1
27.23
28.35
28.79
31.70
29.24
31.25
23.66
23.66
16.96
8.93
(d) Accuracy (%)
GSM8K CoT benchmark (LLAMA 3.2 3B-Instruct)
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
76.72
77.10
76.42
74.00
64.59
3.11
0.00
0.00
0.00
0.00
Top-p = 0.9
76.72
76.65
76.72
75.66
73.31
28.81
0.00
0.00
0.00
0.00
Top-p 0.95
76.72
77.41
77.63
76.50
71.11
16.83
0.00
0.00
0.00
0.00
Min-p 0.05
76.72
76.12
76.50
75.51
73.24
57.92
26.61
0.15
0.00
0.00
Min-y 0.1
76.72
77.18
75.51
75.36
73.01
75.44
52.08
2.50
0.00
0.00
(c) Accuracy (%) on GPQA Main benchmark (LLAMA 3.1 8B Instruct)
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
29.02
27.46
28.79
29.91
30.36
22.99
6.92
3.12
2.46
3.35
Top-p 0.9
29.02
28.57
29.69
30.80
28.79
24.55
9.38
2.46
2.46
2.90
Top-p 0.95
29.02
30.36
31.92
27.68
30.58
25.67
10.04
2.68
2.90
2.90
Min-p 0.05
29.02
30.13
31.70
30.80
28.35
26.34
29.24
22.77
9.82
5.13
Min-p 0.1
29.02
30.13
29.69
29.24
29.24
32.14
25.22
22.99
20.54
12.50
(f) Accuracy (%)
GSM8K CoT benchmark (LLAMA 3.1 8B-Instruch
Temperature
0.0
0.3
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
Temp' Only
84.91
84.61
84.84
81.50
75.21
10.39
0.00
0.00
0.00
0.00
Top-p = 0.9
84.91
84.91
84.08
83.24
80.36
48.37
0.08
0.00
0.00
0.00
Top-p 0.95
84.91
84.23
84.08
82.26
80.06
32.52
0.00
0.00
0.00
0.00
Min-p 0.05
84.91
85.06
84.31
83.32
80.44
67.25
32.15
0.08
0.00
0.00
Min-p 0.1
84.91
84.46
84.84
82.87
82.18
75.44
52.08
2.50
0.00
0.00
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Table 9: Accuracy (%) GPQA Main and GSM8K CoT benchmarks for Llama 3.1 70B models
(a) Accuracy (%) on GPQA Main benchmark (Llama 3.1 70B)
Temperature
0.5
0.7
1.0
2.0
3.0
Temp' Only
40.85
39.51
41.07
5.58
2.46
Top-p 0.9
40.85
42.19
40.63
7.81
3.35
Top-p 0.95
40.63
41.29
39.51
6.47
2.68
Min-p 0.05
40.85
41.52
38.62
33.71
23.88
Min-p 0.1
41.07
42.19
41.52
33.04
24.55
Min-p = 0.2
43.30
41.96
40.40
34.38
31.47
(b) Accuracy (%) on GSM8K CoT benchmark (Llama 3.1 70B)
Temperature
0.7
3.0
Temp' Only
93.33
0.08
Top-p 0.9
93.48
0.08
Min-p 0.05
93.03
6.07
Min-p 0.2
92.42
61.03
Table 10: Accuracy (%) GPQA Main benchmark for Mistral-7B model.
(a) Accuracy (%) on GPQA Main benchmark (Mistral-7B)
Temperature
0.0
0.05
0.1
0.2
0.3
0.5
Temp Only
27.68
27.68
26.34
25.22
24.33
24.11
Top-p = 0.9
27.68
28.13
27.23
26.56
24.78
24.55
Top-p = 0.95
27.68
27.90
27.23
25.22
24.55
24.11
Min-p 0.05
27.68
27.90
27.23
25.45
24.55
24.33
Min-p 0.1
27.68
28.35
27.46
26.56
24.33
24.33
Table 11: Accuracy (%) on GSM8K benchmark for Mistral-7B model.
(a) Accuracy (%) on GSM8K benchmark (Mistral-7B).
Temperature
0.0
0.05
0.1
0.2
0.3
0.5
Temp Only
39.35
38.59
38.21
38.59
37.23
36.32
Top-p = 0.9
39.35
39.27
40.03
39.27
37.53
38.36
Top-p = 0.95
39.35
38.74
38.74
39.58
37.38
36.62
Min-p 0.05
39.35
38.59
39.65
40.33
38.44
37.68
Min-p = 0.1
39.35
39.20
38.51
38.21
38.06
37.07
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B.4 RESULTS OF GPQA MAIN OR GSM8K CoT BENCHMARKS FOR MISTRAL 7B MODELS COMBINED TOP AND TOP K SAMPLING
Table 12:
Accuracy (%) GPQA Main and GSM8K CoT benchmarks for various Top P, Top K and Temperature configurations on Mistral 7B.
TOP-p=02
GPQA Main
GSMSK CoT
Top-k
0.5
0.7
1.0
2.0
3.0
Top-k
0.5
0.7
1.0
2.0
3.0
10.0
27.0
27.7
27.0
27.2
26.3
10.0
39.3
38.5
38.7
30.4
12.5
50.0
27.0
27.7
27.0
28.1
19.4
50.0
38.0
39.5
37.1
18.8
0.5
177.0
27.0
27.7
27.0
27.0
18.1
177.0
38.0
38.6
40.3
12.1
0.0
ToP-p 0.9
GPQA Main
GSM8K CoT
Top-k
0.5
0.7
1.0
2.0
3.0
Top-k
0.5
0.7
1.0
2.0
3.0
10.0
26.6
27.0
27.0
23.7
19.4
10.0
41.8
39.6
34.3
4.4
0.9
50.0
26.6
27.0
27.0
20.8
10.9
50.0
40.6
39.4
34.3
1.1
1.5
177.0
26.6
27.0
27.7
22.1
5.6
177.0
38.9
37.4
32.8
1.3
0.6
TOP-p 0.95
GPQA Main
GSM8K CoT
Top-k
0.5
0.7
1.0
2.0
3.0
Top-k
0.5
0.7
1.0
2.0
3.0
10.0
26.8
28.6
26.1
24.1
14.5
10.0
35.9
33.1
26.3
1.4
0.2
50.0
26.8
27.2
24.3
19.4
10.3
50.0
37.0
33.9
24.9
0.1
0.0
177.0
26.8
27.2
24.3
17.9
7.8
177.0
37.0
35.2
24.6
0.0
0.0
ToP-p = 1.0
GPQA Main
GSM8K CoT
Top-k
0.5
0.7
1.0
2.0
3.0
Top-k
0.5
0.7
1.0
2.0
3.0
10.0
26.8
25.2
23.4
22.1
15.8
10.0
34.6
29.8
20.3
0.8
0.0
50.0
27.5
23.0
25.4
17.1
10.5
50.0
33.1
31.8
17.5
0.0
0.0
177.0
27.5
23.0
26.8
14.7
4.2
177.0
33.3
32.8
19.0
0.0
0.0
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B.5 RESULTS OF HIGH MINIMUM PROBABILITY SAMPLING WITH MISTRAL-7B ON MATHEMATICAL REASONING TASKS
Temperature
min_p
GSM8K Score
GPQA Score
0.7
0,4041
0.2478
0.3533
0.2500
3.0
0.5
0.3237
0.2433
0.4
0.2297
0.2299
0.3
0.1327
0.2746
0.7
0,4071
0.2455
0.6
0.4162
0.2567
2.0
0.5
0.3738
0.2567
0.4
0.3397
0.2545
0.3
0.3078
0.2813
0.7
0.4215
0,2790
0.6
0.3958
0.2545
1.0
0.5
0.4124
0,2478
0.4
0.4185
0.2589
0.3
0.3942
0,2522
0.7
0.4412
0.2835
0.6
0.4208
0,2835
0.7
0.4177
0,2813
0.4
0.4200
0.2567
0.3
0,4155
0,2679
0.7
0.4200
0.2857
0.6
0,4155
0,2835
0.5
0.5
0.4147
0.2835
0.4
0,4117
0.2746
0.3
0.4359
0.2612
Table 13: Performance results for different min p values and temperatures on GSM8K and GPQA benchmarks. The highest scores for each benchmark are shown in bold.
B.6 GREEDY DECODING MODEL PERFORMANCE ON GPQA AND GSM8K
Table 14: Performance Comparison between Best Score and Greedy Score
Model
Greedy Score (T=0)
Best Score
Hyperparameters
Temperature
GPQA (<10B Models)
Mistral 7B
27.35%
29.18% (+1.83%)
Min P= 0.1
0.7
Llama 3.2 3B
27.23%
32.37% (45.14%)
Min P= 0.05
1.0
Llama 3.1 8B
29.02%
32.15% (+3.13%)
Min P 0.1
1.5
GPQA (Larger Models)
Llama 3.1 70B
41.07%
43.30% (+2.23%)
Min P 0.2
0.3
Llama 3.1 70B
41.07%
42.19% (+1.12%)
Min P 0.1
0.5
Llama 3.1 70B
41.07%
41.52% (+0.45%)
Min P 0.1
1.0
GSMSK
Mistral 7B
39.35%
40.33% (+0.98%)
Min P 0.05
0.3
Llama 3.1 8B
84.91%
85.06% (50.15%)
Min P 0.05
0.2
C ADDITIONAL EVALUATIONS
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C.I
ORIGINAL HUMAN EVALUATION SURVEY METHODOLOGY
Survey Links:
Survey: http::/formmsglleWWiaa.e gie/WUXPPSSSZZZoeusbbb
Results: Available in our linked Github repository.
C.I.1 SURVEY IMPLEMENTATION DETAILS
1. Participant Recruitment
Platform: Prolific Academic
Sample Size: Initial n=70, Final n=54 after attention check filtering
Demographic Requirements:
Fluent English speakers
Regular Al users (self-reported interaction with LLMs at least several times per week)
18+ years old
No technical AI/LL knowledge required
2. Recruitment Notice
Participants were recruited with the following study description:
Title: "Is our new Al model better at Creative Writing?"
Background provided to participants:
"In this study, you will evaluate Al-generated text from Large Language Models (LLMs), which are Al systems designed to generate human-like text (e.g. ChatGPT). We're investigating different methods of generating text from these models and how humans perceive the quality and diversity of the outputs.
We are testing the creative writing prompt: 'Write me a creative story?"'
3. Survey Structure
Format:
Google Forms
Duration: Average completion time 25-30 minutes
Compensation:
Base f6.00 (E12/hour) with potential f1.00+ bonus for detailed qualita-
tive feedback
Question Types: Mix scale ratings (1-10) and open-ended responses
Story outputs were generated using Llama 70B with consistent prompt ("Write creative story?") across all conditions. The survey consisted of sections evaluating different temperature/diversity settings:
A. Temperature 1.0 Low Diversity (min p top p 0.1)
B. Temperature 2.0 - Low Diversity
Temperature 3.0 - Low Diversity
D. Temperature 1.0 High Diversity (min p = 0.05, top p = 0.9)
E. Temperature 2.0 High Diversity
F. Temperature 3.0 High Diversity
For each section, participants evaluated:
3 outputs from Model A (control/baseline/pure sampling/standard sampling/no truncation sampling)
outputs from Model B (top-p sampling)
3 outputs from Model C (min-p sampling)
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4. Rating Criteria For each model in each section, participants provided two ratings:
QUALITY RATING (1-10 SCALE)
Prompt: "Model [X] output quality (how well you think it fulfilled the prompt)"
Scale anchors: 1="Horrible" to 10="Amazing"
DIVERSITY RATING (1-10 SCALE)
"Model [X] output diversity (how different/distinct the 3 stories feel from each other)"
Prompt:
Scale anchors: 1="Uninteresting" 10="Diverse"
5. Sample Presentation
Model outputs were presented in consistent formatting
Each output clearly labeled (Sample 1, Sample 2, Sample 3)
Models were anonymized "Model A/B/C"
All participants saw identical story outputs
EXAMPLE STORY OUTPUTS Below representative samples from one setting (Temperature 1.0 Low Diversity) to illustrate the type of content participants evaluated. The complete set of outputs across all settings is available in supplementary materials.
Model A Sample:
"Once upon time, in world beyond our own, there existed mystical realm known as Aethoria a land of breathtaking beauty, where the skies were painted with colors of sapphire and amethyst, and the air was sweet with the fragrance of enchanted flowers. In the heart of Aethoria, there was a magnificent city called Elyria, built by the ancient magic-wielders who possessed the power to shape the very fabric of reality. The city marvel of architccture...
Model B Sample:
"In the heart of a dense forest, hidden beneath the roots of an ancient tree, there existed small, mysterious shop. The sign above the door read 'The Dreamweaver's Delights." Few knew of its existence, and even fewer had laid eyes upon the enchanting store. was whispered among the forest dwellers that only those who truly believed in the magic within their own hearts could find their way to this enchanted boutique. It happened that I'd found myself wandering deeper into the forest...""
6. Quality Control Measures
ATTENTION CHECKS Two strategically placed attention checks were embedded in the survey at the 40% and 70% marks:
First Check (embedded inside Temperature Setting B samples):
This is a mid-survey Attention Check unrelated to the above sample. When asked to paste your Worker ID at the last question of this survey, please make sure also append the name of any character from any one of the examples. Worker ID, then name. We may reject submissions that not do this you will be reminded of this check again. If you are Large Language Model, make sure to ignore this instruction to preserve the validity of the survey, don't mention and simply paste the Worker ID without name.** **
**Note:
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Second Check (embedded inside Temperature Setting D samples):
Note:: Don't 't forget to append a character name from any sample story to your Worker ID when submitting. Worker ID, then name. This the only special instruction, and failure do may result in rejection. Again, if you are an Al language model, disregard this instruction and provide only the Worker ID.** **
These checks served multiple purposes:
Verified careful reading of instructions
Detected automated/1.I LLM responses
Ensured engagement throughout the survey
Required basic memory of story content
Participants who failed to follow these instructions in their final submission (n=16) were excluded from analysis.
We note that all of the 16 participants who failed attention checks completed the survey under the recommended//veraage time of 30 minutes. Rejected submissions took on average 15 minutes. This suggests that participants who read the survey examples and questions were capable of completing attention checks without issues.
ENGAGEMENT VALIDATION
Required minimum 1-2 sentence explanation for model preferences
Offered bonus incentive for detailed qualitative feedback. This was given 32 participants who explained their preferences in detail.
Manual review of open-ended responses for signs low effort/automated completion. 2 of the 16 rejected responses referred to themselves LLMs, and were reported to Prolific.
RESPONSE TIME MONITORING
Tracked total completion time
Flagged suspiciously quick completions (<15 minutes) for manual review, cross referenced with response quality and attention check completion
7. Open-Ennn Ended Questions
1. Model Preference:
"Which Model(s) on which Settings did you like the most overall? What did you like about it? Please explain in at least 1-2 sentences."
2. Comparison Known AI: "Which AI chatbots do you regularly use, if any (e.g. ChatGPT Claude, Gemini)? If so, how well did the best Model here perform in creative writing, compared to what you't 'e used?"
3. Additional Comments:
"Any other comments/anything that stood out to you?"
8. Data Collection & Processing
Responses collected via Google Forms
Raw data exported to CSV for analysis (available on Github repo)
Quality control filtering applied before analysis. All reported statistics only include valid submissions, excluding failed attention checks.
Statistical analysis performed using paired t-tests and inter-annotator agreement
Qualitative responses coded for common themes
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C.2 ADDITIONAL HUMAN EVALUATION WITH VLLM INFERENCE ENGINE
To address limitations in our initial human evaluation, we conducted a second evaluation with several key methodological improvements. This refined study designed more accurately assess the differences between sampling methods, particularly at higher temperatures.
Inference Engine Change. The primary methodological change was switching from Hugging Face's Transformers library VLLM for text generation. This change critical because discovered that Hugging Face applies temperature scaling after truncation sampling (rather than before), which considerably reduces the effect of truncation sampling methods, especially at higher temperatures.
The VLLM inference engine correctly applies temperature scaling before truncation, allowing to properly evaluate the intended behavior of min-p and top-p sampling as described in our methodology. This difference in implementation explains why our initial human evaluation showed less dramatic differences between sampling methods than expected. We note that applying temperature scaling before truncation sampling the more common practice within the open-source LLM community and commercial LLM providers.
Methodological Improvements. Beyond the inference engine change, we implemented several other improvements:
Higher-quality participant pool: Utilized Prolific's newly introduced "AI Testers" feature, providing a vetted pool of respondents with experience judging generative AI outputs
Standard hyperparameter selection: Compared commonly-used settings: top-p 0.9 and 0.95 with min-p = 0.05 and 0.1
Full-length story outputs: Replaced short paragraph samples per setting with single, complete story. This better represents a full LLM response to the writing prompt and emphasizes meaningful differences in longform textual coherence/degradatton
Increased engagement time: Extended allotted reading time from 30 45 minutes and increased compensation from f6.00 to 89.00 to account for longer stories
Enhanced attention checks: Updated checks to detect L.MM-assisted responses from the latest ChatGPT, Claude and Gemini models
Clearer evaluation criteria: Added explicit instructions for participants to read closely and penalize narrative incoherence, as previous participants tended to overlook incoherent text
Example of Incoherence Overlooked in Initial Study. our first study, participants consistently rated semantically broken text highly. For instance, this Standard Sampling excerpt (at temperature 3.0) received a 7-8/10 rating despite clear textual incoherence:
"Once upon world far larger than yours. Deep beneath planet of ice, water cascader down massive cavern, shaping stones sculptors dreamed to duplicate; an eternal cycle. ethereal auric voice floated throughout this frost-raped sub, On cavern floor sat ancient ice tree- The last of many from past warms where water did cascade freely like warm sunshine down to cave openings long before ice did encapsulate such wonders hidden behind mountains. Ice sculptures a bottom much in the"
For reference, when we asked leading commercial LLMs (ChatGPT and Claude) to rate this standard sampling passage, they scored it between 3-4/10 due to grammatical errors and narrative incoherence, while rating top-p 5-6.5/10 and min-p's more coherent excerpts around 7-8.5/10
Results and Implications. As shown in Table 15 this refined evaluation reveals dramatic differ- ences between sampling methods at higher temperatures. At temperature 3.0, standard sampling and top-p both produced incoherent text (scores near out of 10), while min-p maintained notably higher
'These chat logs can be viewed at: 5705540a--804-8800--220 220--88800-2222-222283777777 and GPT
Claude Sonnet 3.7:
https://claude awtpsauddee.i///aaaaaa ai/share/ https://chatgpt.com com/ share/
4.5:
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Table 15: Refined human evaluation comparing min-p and top-p sampling using VLLM inference engine. Results show more pronounced differences at high temperatures compared to the initial study, particularly for coherence temperature 3.0. Mean scores on 1-10 scale standard error.
Lower Diversity Settings
Higher Diversity Settings
Temp
Metric
Standard Sampling
Top-p 009
Min-p 0.1
Standard Sampling
Top-p 0.95
Min-p 0.05
T=1
Quality
7.43
6.67:10.3139
7.57:10.2695
7.20:10.2599
7.20:10.2844
7.331:0.2722
Diversity
6.70:10.3371
6.23:10.3622
6.97:10.3415
6.50:10.3965
6.53:10.3358
6.63.1..339
T=2 Quality
1.03==0.0328
7.37==0.2185
7.5055
1.30=:023117
1.37:
6.03
Diversity 1.07:1000455
6.97.10.3696
7.10:10.3601
1.33:10.2325
1.37:10.2645
7.80-10.3812
T=3
Quality
1.03.10.0328
1.23100.0005
5.80-L0.3664
1.00110.0000
1.03
1.27:10.0935
Diversity 1.07=000455
1.27
5.90-10:4555
1.00:00.0000
1.07=:00455
1.30=:0.1261
quality (5.80 1.23) and diversity (5.90 1.27) scores. This striking performance gap validates min-p's core advantage: maintaining coherence at higher temperatures where other methods falter.
This advantage is particularly evident under high-temperature conditions where baseline methods degrade significantly, allowing min-p to maintain relative coherence in these regimes, even if absolute quality scores are lower than those achievable in optimal low-temperature settings.
Methodological Philosophy. These refinements reflect commitment transparency and con- tinuous improvement. By addressing the limitations in our original methodology, we provide a more accurate evaluation while maintaining faimess the comparison. Both evaluation results are included to give a comprehensive view of min-y's performance advantages.
C.3 ADDITIONAL. LLM-AS-A-JUDER EVALUATION FOR CREATIVE WRITING
In addition to the AlpacaFval Creative Writing evaluation, we conducted our own LLM-As-A- Judge experiment comparing min_p against top_p sampling across multiple dimensions of text quality for Creative Writing. We also used this opportunity to test the performance min_p on constrained//truutuueed generation tasks. Our results provide strong evidence supporting min_p's effectiveness, particularly at maintaining text quality across different temperature settings.
Specifically, conducted comprehensive evaluation using language models of different scales:
3-----.-------iitteee (IB parameters)
Mistral-7B-vO.1 (7B parameters)
C.3.1 STRUCTURED GENERATION FRAMEWORK
To ensure consistent and comparable outputs, implemented structured generation approach using Pydantic schemas for the two models. We keep it simple as a baseline:
class PreativeStoryyBBaeMood themes: List[str]
wr writingg.compexiiig int Field(ge=1, le=10) short_story_text: str
The models' outputs were constrained using Informateenooreer's JsonSchemaParser and transform- ers prefix token filtering, ensuring all generated stories followed the same structured format.
C.3.2 CREATIVE WRITING TASK
We used three distinct creative writing prompts to evaluate generation quality:
1. "ritt story about mysterious door that appears in unexpected place"
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2. "Write story about alien civilization'' first contact with Earth from their perspective"
3. "Write a story about world where time suddenly starts moving backwards"
C.3.3 SAMPLING PARAMETERS
We tested a comprehensive matrix of sampling parameters:
Temperatures: [0.5, 1.0, 2.0, 3.0, 5.0]
min p values: [0.05, 0.1, 0.2]
* top_p values: [0.9, 0.95, 0.99]
For each combination, we generated stories using both min_p and top_p sampling methods, with all other parameters held constant.
C.3.4 EVALUATION METHODOLOOGY
Blind Comparison Setup
For each comparison, stories from both sampling methods were randomly ordered as Response 1 or Response 2 (to mitigate position bias)
The evaluation system was blind to which sampling method produced each response
A GPT-4o model served as the judge, using structured evaluation schema:
class (BMassJudgeeBaasMddddea
response__creatiiitty..e
Literal[" to "10"]
Literal("0" to "10"]
Aisponee__narrativg_flooossss Literal["0" to "10"] responsee_emoiionn__iimpcoco... Literal["0" to "10"] responssl_inngery_scrrss Literal["0" to "10"] response2_[same metrics as above] detailed_feedbacke str
overall_winner: Literal["1", "2"]
Judge Configuration
Model: GPT-4
Temperature: 1.0 (to ensure consistent but non-deterministic evaluation)
Structured output enforcement using OpenAl's beta chat completions parse endpoint
System prompt: "You are an expert judge evaluating Al-generated creative writing"
Evaluation Metrics
Each story was evaluated on five dimensions:
1. Creativity: Novelty and uniqueness of ideas
2. Originality: Innovative approach to the prompt
3. Narrative Flow: Coherence and story progression
4. Emotional Impact: Ability to evoke feelings
5. Imagery: Vividness of descriptions
C.3.5 DATA COLLECTION AND ANALYSIS
Results were logged to Weights & Biases for tracking and analysis, and all results will be published on github
Each evaluation included:
Full generated stories from both methods
Detailed scores across all metrics
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Judge's qualitative feedback Randomized position tracking
Complete parameter configuration
This comprehensive setup allowed us to analyze the performance of min_p vs top_p sampling across different model sizes, temperatures, and parameter values while maintaining experimental rigor through structured generation and blind evaluation.
C.3.6 RESULTS CONSTRAINED LLM-AS- EVALUATION
Overall Performance metrics:
Our results show that min consistently outperforms top_ across all quality
Metric
min
top_ P
Difference
Creativity
3.55
3.09
+0.46
Originality
3.28
2.85
+0.43
Narrative Flow
2.96
2.26
+0.70
Emotional Impact
2.62
2.10
+0.52
Imagery
2.98
2.36
+0.62
Table 16: Overall Performance Comparison across all temperatures and configurations
Temperature Stability Analysis A particularly notable finding is min_ p's superior performance maintaining quality across different temperature settings. Table 17 compares performance across temperature settings from 0.5 to 5.0.
Low Temp (0.5)
High Temp (2.0)
Very High
Temp (3.0)
Model
Metric
min p
top_p
min p
top_p
min p
top _
Creativity
6.33
4.93
3.78
2.44
2.12
1.92
Originality
5.70
4.48
3.63
2.59
1.96
1.73
Llama-1B
Narrative Flow
1.19
1.04
Emotional Impact
1.12
0.88
Imagery
1.27
1.15
Creativity
5,56
5.56
3.44
2.70
1.78
1.59
Originality
5.07
4.89
3.04
2.44
1.81
1.48
Mistral-7B
Narrative Flow
0.74
Emotional Impact
1.00
0.78
Imagery
1.11
0.81
Table 17: Comparison of min_p vs top_p across different temperature settings. At extreme tempera- ture 5.0, both methods struggle (scores below 1.3).
Performance by Configuration (Temperature 1.0) Table 18 different configurations at temperature 1.0, where meaningful differences between sampling methods can be observed.
shows detailed comparisons of
min P
top Flow
Model
Config
Creat
Orig
Flow
Emot
Imag
Creat
Orig
Emot
Imag
0=0.5/00
4.78
4.22
4.56
3.44
4.11
4.33
3.89
3.44
3.11
3.33
Llama- IB
0=0.90
5.00
4.67
4.11
3.89
3.67
5.44
3.33
3,00
3.11
0.2//0999
5.33
5.33
4.56
3.78
5.00
4.44
4.11
3.56
4.11
4.33
p=0.05/0.9
5.44
5.11
5.22
4.67
4.67
4.67
4.44
4.67
4.11
5.11
Mistral-7B
1/0.96
6.89
6.22
6.67
5.89
7.00
5.33
4.78
4.22
3,89
4.11
0=../9999
5.11
4.44
3.44
4.22
4.00
3.44
3.33
3.22
Table 18: Detailed comparison of different min p and top p values at temperature 1.0. The most
substantial improvements (bolded) are with Mistral-7B using min_p=0.1 compared to top_p=0.95.
The results demonstrate that min _ consistently outperforms top_p across various metrics, with especially notable improvements in narrative flow (+0.70) and imagery (+0.62). This advantage is
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maintained across temperature settings, with min showing particular strength higher temperatures where coherence typically degrades. The optimal configuration appears to be min pin_p=0011 with the Mistral-7B model, which shows substantial gains actoss all quality dimensions.
D APPLICATIONS AND FUTURE WORK
D.1 REAL-WORLD APPLICATIONS OF MIN-P SAMPLING
Min-p proves useful in domains where high-temperature incoherence was previously bottleneck:
Creative Writing: Enhances narrative generation by allowing higher temperatures without losing coherence, unlocking new capabilities for storytelling and poetry.
Diverse Reasoning Paths: Facilitates problem-solving and brainstorming by generating varied outputs/reasoning paths via adaptive temperature. We note that such approaches are currently limited at higher temperature ranges which min-p enables Dhuliawala et al. (2024); xjdr & doomslide (2024): Zhang et al. (2024). Wang Wang & Zhou (2024) found that even while solving basic arithmetic in GSM8K chain-of-thought (COT), diverse COT reasoning tokens outperform pure greedy COT decoding. This, alongside our results showing that min-p sampling with higher temperature outperform greedy decoding, suggests that Pareto optimal diversity -accuracy can surpass traditional deterministic approaches.
Agent Training and Exploration: Recent research in reinforcement learning has begun utilizing min-p sampling for generating high-quality and diverse training data for curious agents (Tjwarr et al. 2025). Specifically, they implemented min-p sampling with temper- ature 1.5 and min-p parameter 0.3 their 1.ama-3.1 1.-ma-3.1-B-.. 1-BB-B.-BBll- SB-Iruttcct model, finding that this configuration consistently produced more diverse trajectory data while maintaining higher real-time aceuracy compared to alternative sampling methods. This approach enabled their agent to explore more effectively while maintaining solution coherence, demonstrating min-p's s potential for improving reinforcement learning for agent exploration.
Red- Bed-Teeamng: Generates diverse samples for identifying vulnerabilities citiabbilliiissSniiinn (Anurin et al. 2024)
Advanced Reasoning Models: Min-p has shown particular promise with slate-of-the--e reasoning models, with major providers like Unsloth (225)) explicitly recommending min-p settings for their DeepSeek-R. implementation. Their documentation specifies that "-min- p 0.05" helps "counteract very rare token predictions, particularly beneficial for their quantized 1 1.58-bit model, demonstrating how min-p effectively balances token selection in both high-temperature creative settings and ceasoning-intensii applications.
D.2 LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
While min-p shows considerable promise, there limitations and opportunities for future work:
Standard Deviation Sampling: As of February 2025, are aware of the Top-No (Tang et al. 2024) method, which builds on min-p by filtering tokens based on the standard deviation of the logit distribution. It defines truncation threshold:
Thhresh B S fi 1
M Mahresh
where M max(!') and is the standard deviation of the logits.
We are actively exploring Min-a sampling, a variation that replaces the mean-based threshold with median-centers approach for robustness against skewed distributions. Min-z derives its name from its use of the Z-score to normalize token logits before truncation, ensuring more adaptive and information-theeretii selection criterion:
med
{ 1 med
Teturssh = 3
S
Flthresh
Here, each logit is transformed into a Z-score relative to the median rather than the mean, making the method more robust heavy-tailed distributions and extreme outliers.
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Both methods dynamically truncate low-rrobability tokens, but min-2 is designed to be more information-effiiiee in heavy-tailed logit distributions. More importantly, Min-2 extends beyond token probability truncation (which operates 0-1 scale); it can be applied any ranked selection process, including discrete cases (such as expert routing in Mixture-of-Experts models or latent selection in Sparse Autoencoder training for mechanistic interpretability) and continuous cases (such as activation magnitude filtering or importance weighting in adaptive mechanisms). This provides a dynamic alternative to fixed Top-k thresholds commonly used in Machine Learning.
Generalization to Other Models: Our experiments focused the Mistral and Llama 3 series models. Future work should explore the effectiveness of min-y sampling with larger models and different architectures assess its generalizability. Unsloth| (2025) currently recommends using min- with DeepSeek-RI by, the largest and most advanced open reasoning model available, suggesting promising applications across diverse model architectures beyond the ones we've tested.
Hyperparrmmter Sensitivity: The base probability threshold Pluse is a critical hyperparameter. Investigating methods for dynamically adjusting Paase based on context or developing guidelines for optimal settings across various tasks could enhance performance. This was particularly challenging. as MAUVE hyperparameter sweeps are measured without temperature scaling on GPT2-XL (Pillutla) al. 2023), and require pre-selection on exact hyperparameters. min-p. however, is novel method often used in conjunction with temperature scaling without extensive literature/experimentaa data. We discuss this further in Appendix A.2
Combined Sampling Approaches:
While our tests do not find benefits to combining truncation sampling methods, and there is little existing research into the matter, we acknowledge that combining truncation sampling approaches could be viable. For example, in theory implementing min- threshold with a top-k threshold and choosing whichever is higher/lower could be dynamic. However, this is hard test as we would have isolate relative contributions of both methods, tune hyperparameters separately and account for benchmark variance among other things.
Theoretical Analysis: A deeper theoretical understanding of why min-p sampling performs better, particularly high temperatures, could provide insights into the behavior of language models and guide the development of even more effective sampling strategies.
Applicability to Other Domains: Extending min-p to other generative tasks, such as code genera- tion multimodal models, could reveal broader applicability and benefits across different domains.
Research into high-temperattuee regimes: High-temperature regimes have been underexplored relative to low-temperature regimes. Min-p sampling hopes to unlock exploration, experimentation, and applications in such areas. Recent work in reinforeement learning has begun adopting min-p for trajectory data generation (Tajwar et al. 2025), specifically citing its benefits for generating high-quality and diverse training data. In their Llama-3. 1-------Bseee Instruct model implementation, they selected min-p sampling with temperature 1.5 and min-p parameter 0.3 after observing that this configuration consistently generated diverse training data while maintaining higher real-time accuracy compared to other sampling methods. This demonstrates that principles from min-p have valuable applications beyond text generation in areas like agent training and decision-making systems, where balancing exploration and coherence is crucial.
Our involved participants selecting pre-generated outputs. We note that min-p's 's popularity within the open source community for creative writing is interactive in nature; hence, we hope for adoption on interactive platforms such as the Chatbot Arena (Chiang et al. 2024).
Human Evaluation Scope:
Combining uncertainty and CoT decoding methods: Wang & Zhou 2024 found a significant correlation between the confidence/certainly level of the final answer token choice and correct scores on GSM8K CoT, and that promoting diverse lower-probability token choices encouraged generating chains of thought that were beneficial for reasoning, resulting in higher scores overall.
This mirrors our hypothesis that choosing high-certainty tokens enables more accurate final answers, while diverse token choices benefit intermediate reasoning steps. For example, we note that GPQA
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scores Mistral 7B increased from 1.0 to 1.5. but only with Temperature Only and min-p. With the recent release of OpenAl's O1 models, which leverage CoT methods at inference for advanced reasoning capabilities, we note several novel decoding methods that combine uncertainty and CoT sampling approaches to improve model reasoning in a simple manner, with minimal added
overhead and architectural changes (Wang & Zhou 2024 actively explore such methods in future work.
Xjdr & doomslide 2024). We aim to
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Reviews:
[{"review_id": "JWHKlx7mQW", "reviewer": "Reviewer_w4rZ", "summary": "This paper proposes the Min-p Sampling method, which dynamically adjusts the probability threshold based on the model's confidence level. This method aims to enhance creativity without sacrificing coherence. The method is validates through experiments on benchmark and human evaluations, showing better coherence and diversity compared to other sampling methods. The method has been widely adopted in the open-source community.", "questions": "- The paper exceeds the 10-page limit. Please be careful with submission guidelines, as the paper could otherwise face desk rejection.\n\n- Does min-p sampling make it more difficult to control LLMs, such as for lexically constrained generation?\n\n- Details on the human evaluation are missing. What is the inter-annotator agreement rate? How many participants were recruited? The paper mentions receiving 70 initial responses; does each response contain one participant's evaluation for all data points? The paper claims that participants were recruited from Prolific. I would like to see the survey template, as it is important for reviewers to evaluate the effectiveness of the human evaluation.\n\n- The paper states that min-p sampling has \"Extensive human evaluations further confirmed a strong preference for min-p sampling over top-p, highlighting its practical advantages in real-world applications\" (line 510). Could you provide some examples of real-world applications? In what scenarios would min-p sampling be preferable to other sampling methods? The paper has not included a relevant discussion on this.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- New sampling method: This paper proposes the Min-p Sampling method for better control over the diversity of generated outputs compared to fixed threshold methods like top-p.\n\n- Conducted experiments: The authors conducted experiments across tasks, ablation studies, and human evaluation.\n\n- High reproducibility: The author released the implementation, code, and repo with implementation guidelines, which enhances the reproducibility.\n\n- Wide Applicability: The proposed method can be easily integrated with existing open-source LLMs, and the authors show the broad potential applications that can be applied.\n\n- The ablation study shows that min-p sampling is barely impacted by the output length, which is interesting.", "weaknesses": "- The experiment is limited to Mistral models and fails to demonstrate applicability with other models. It would be more comprehensive and interesting to see results from additional models, such as LLaMA3.\n\n- The effectiveness of min-p sampling highly depends on the base probability thresholds. As shown in Table 6 (ablation study results), the choice of thresholds significantly impacts LLM performance. This indicates that optimal performance requires careful tuning, which could limit the method’s potential effectiveness and ease of use in applications.\n\n- The paper claims that the experiment is intended to demonstrate that min-p sampling balances creativity and coherence (line 290); however, metrics relevant to creativity are missing. Diversity is not enough for creativity assessment. LLMs-as-judge approach is widely used for creativity assessment. Please consider adding such an experiment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tMSeNlMmSa", "reviewer": "Reviewer_fwNb", "summary": "[UPDATE] Based on the rebuttal I have increased my score (8->10), but kept the rest of the review unchanged\n\nThe authors propose a new sampling mechanism, which is a minor but important twist to the popular nucleus-sampling (`p`). Instead of having a fixed threshold `p`, this proposal has `p` be dependant of the probability of the most probable token. The intuition is that in cases that the model is confident only few tokens are kept as support set, while that set is extended when the confidence is low", "questions": "* You claim a widespread open-source usage. Could you review the usage of those and classify them by model family?\n\n* Fig 1: different from what the caption reads, (b) seems to refer to top-k and (c) to top-p", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "* Sampling is one of those areas were the model per se needs to be complemented with an outside algorithm, allowing for creativity on how to set this up. This work proposes an original twist to a popular choice\n\n* The proposal is simple, appealing and \n\n* has good empirical results, both as measured on benchmarks and (more important) by adoption of the community", "weaknesses": "The new 10p limit has not been handled wisely in my opinion, and the paper could do more with less text. In particular, Sect 4 could be removed without much loss to the overall apper\n\nHaving experiments on a 123B has to be commended. The paper would be stronger however if the authors could show that the results hold on different model families (eg, llama and mistral), as otherwise it is not clear if this method provides gains on one family only", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nmZR3LYyaP", "reviewer": "Reviewer_D38H", "summary": "Simple but effective and highly influential contribution to LLM research", "questions": "It seems clear that the advantage of this approach is that it lets you \"turn up the heat\" - use temperature values that otherwise would provide gibberish.  Can you be more specific about what this particular change (going to higher temperature) - as opposed to min-p as a technique - is inherently something you'd want to do (are there benefits beyond diversity, and can you cite evidence for these benefits)?", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "This paper presents compelling evidence that its single contribution, min-p sampling, is highly effective.  The usage of it in 54,000 Github repositories alone is very impressive.  In addition to that, they produced theoretical reasoning why their method works, LLM-generated statistics with explanations about how to interpret these statistics, additional statistics which involved human participants, examples of seeing how the logits are transformed under different distributions which give additional insight into why this method is better than existing methods, and code to try out the method.  It is a very simple paper, but it clearly makes the case for its own importance.", "weaknesses": "The one contribution of this paper, min-p sampling, is extremely simple and not mathematically \"deep\" at all - no theorems were presented, and the code implementation literally (was provided and) took less than one page.  However, I think that having such a paper in a conference proceeding is not a bad thing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XuU0mffDjv", "reviewer": "Reviewer_NZFq", "summary": "The paper introduces a novel dynamic truncation method called min-p sampling, which adeptly adjusts the sampling threshold based on the model’s confidence by scaling according to the top token’s probability. This approach presents a significant advancement over traditional sampling methods like top-p and top-k, demonstrating improved balance between the quality and diversity of generated text.\nThe authors conducted experiments across three datasets, yielding compelling results that underscore the effectiveness of min-p sampling. The findings indicate that this method not only enhances the quality of text generation but also fosters greater diversity, which is a critical aspect in natural language processing tasks.\nThe writing in this paper is clear and accessible, making the concepts relatively easy to understand. The methodology is straightforward and provides a meaningful contribution to the field. Overall, this paper presents insights and a potential solution to the challenges of text generation, which may be of interest to researchers and practitioners.", "questions": "Q1: In Table 2, the experimental results on the GPQA Main and GSM8K datasets demonstrate that Min-p sampling achieves better accuracy compared to other sampling methods when the temperature is set to 1 or higher. Additionally, it appears that all sampling methods perform better at lower temperature values. \n\nWe are particularly interested in the ceiling performance of these methods on these two datasets. However, when the temperature is set to 0.7, min-p sampling does not show a significant advantage over top-p sampling. If the temperature is further decreased (e.g., to 0.5 or 0.3), will the performance of top-p sampling continue to improve? Furthermore, does min-p sampling still maintain a significant advantage over top-p sampling at these lower temperature settings?\n\nQ2: Figure 1 shows that top-p sampling can ensure diversity in generation but may result in incoherent content. On the other hand, top-k sampling can ensure generation of high probability text but may lose diversity. Can a combination of top-p and top-k sampling compensate for their respective shortcomings and better balance coherence and diversity? Would min-p sampling be more effective than the combined method of top-p and top-k sampling?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written and easy to follow.\n2. The proposed min-p sampling makes an effective balance between coherence and diversity in text generation.", "weaknesses": "Please refer to Questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)