EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Authors: Yuhui Li
Fangyun Wei
Chao Zhang
Hongyang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Speculative decoding
Primary area: deep_learning
TL;DR: We propose EAGLE-3, observing that it can benefit from scaling up the data.

Abstract: The sequential nature of modern LLMs makes them expensive and slow, and speculative sam- pling has proven to be an effective solution to this problem. Methods like EAGLE perform autoregression at the feature level, reusing top- layer features from the target model to achieve better results than vanilla speculative sampling. A growing trend in the LLM community is scaling up training data to improve model intelligence without increasing inference costs. However, we observe that scaling up data provides limited improvements for EAGLE. We identify that this limitation arises from EAGLE’s feature prediction constraints. In this paper, we introduce EAGLE-3, which abandons feature prediction in favor of direct token prediction and replaces reliance on top-layer features with multi-layer feature fusion via a technique named training-time test. These improvements significantly enhance performance and enable the draft model to fully benefit from scaling up training data. Our experiments include both chat models and reasoning models, evaluated on five tasks. The results show that EAGLE-3 achieves a speedup ratio up to 6.5x, with about 1.4x improvement over EAGLE-2. In the SGLang framework, EAGLE- 3 achieves a 1.38x throughput improvement at a batch size of 64.

EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Yuhui Li Fangyun Wei Chao Zhang², Hongyang Zhang University of Waterloo "Peking University Microsooft Research Vecctor Institute yuhui li@stu..uu.. Li@stu.pku. pku. edu en, fawe@microsoff com c z.angg@pp en, hongyang. zhangduxaterloo..c
Abstract
The sequential nature modern LLMs makes them expensive and slow, and spec- ulative sampling has proven to be an effective solution to this problem. Methods like EAGLE perform autoregression at the feature level, reusing top-layer features from the target model to achieve better results than vanilla speculative sampling. A growing trend in the L.M community is scaling up training data improve model intelligence without increasing inference costs. However, we observe that scaling up data provides limited improvements for EAGLE. We identify that this limitation arises from EAGLE's feature prediction constraints. In this paper, introduce EAGLE-3, which abandons feature prediction in favor of direct token prediction and replaces reliance top-layer features with multi-layer feature fusion via a technique named training- time test. These improvements significantly enhance performance and enable the draft model fully benefit from scaling up training data. Our experiments include both chat models and reasoning models, evaluated five tasks. The results show that EAGLE-3 achieves speedup ratio up 6.5x, with about 1.4x improvement over EAGLE-2. In the SGLangg framework, EAGLE-3 achieves 1.38x throughput improvement at batch size of 64. The code is available at littttt://// /ttt:::///tttut github......... com Saf Saff eAILab/EAAEE
Speculative sampling
EAGLE-3
5.6x
5.0x
4.1x
Speedup
3.1x
3.6x
3.2x
2.8x
2.1x
1.0x
1.0x
1.0x
1.0x
1-
Vicuna 138
LL MaM---nsttu 3.1
LLaMA-Instruct 3.3 708 DeepSeek R1 LLaMM 8B
Figure 1: Speedup ratios of different methods at temperature=0. For the standard speculative sampling, Vicuna- 13B uses Vicuna-68M as the draft model. Table [ we present comparisons with additional methods, but this figure only showcases subset. Chat model's evaluation dataset is MT-bench, and reasoning model's evaluation dataset GSM8K. DeepSeek RI LLaMA refers to Dinrssck-RR--isill-LLLLLL 8B.
Introduction
Modern Large Language Models (LLMMs) are being applied to more domains, with their improved capabilities driven by scaling model parameters some LLMs now exceed hundreds of billions of
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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4.4
4.2
Accept length
6.0
5 4.0
5.5
EAGLE-2
EAGLE-2
3.8
EAGLE-3
5.0
EAGLE-3
8.6
3.4
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3.2
4.0
2
Figure 2: Scaling law evaluated on the MT-bench using LLaMA-Instruct 3.1 8B as the target model, with the x-axis representing the data scale relative ShareGPT. The architectural designs in EAGLE-3 enable an increasing scaling curve, which was never observed in the previous works.
parameters. In autoregressive generation, each token requires accessing all model parameters, making LLM inference slow and costly.
Recently, test-time scaling up has gained significant attention. Models like ChatGPT and DeepSeek- RI [ engage in deliberate reasoning before responding, pushing the boundaries of LLM capabilities at the cost of longer inference time. However, these models often require lengthy reasoning processes, making them extremely costly, while the increased response time severely impacts user satisfaction. These reasoning models significantly increase the proportion inference costs in the overall LLM pipeline, driving researchers to explore cheaper and faster inference optimization methods.
Speculative sampling methods can reduce LLM latency by partially parallelizing the generation process. These methods rapidly generate draft tokens and then verify them in parallel. This al- lows multiple tokens to be produced in a single forward pass, significantly reducing inference la- tency. As a state-of-ie the--att speculative sampling method, EAGLE [2] decodes an LLM by lever- aging the top-layer features of the target model (i.e., the representations before the LM head) to perform next-feature prediction. As shown in the first box in Figure EAGLE trains the draft model by feeding all previous features fi...., ft into the target model to predict the next feature fe+1, with the training data feature fi+1 as the label. In the test phase (the second box), EA- GLE autoregressively predicts the next feature and then uses the target model's s LM head obtain the draft token f. By leveraging the rich information from the target model, EAGLE achieves significantly better acceleration com- pared to vanilla speculative sampling. Subse- quent methods such HASS [3] and Falcon 4 also adopt the approach of predicting the next feature using the current feature sequence.
Broft
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Recent LLMs have increasingly relied on larger training datasets achieve better performance. For example, a.M series models with sizes of 7B (8B) have used IT, 2T, and 15T tokens of training data for LLaMA (5). LLaMA 2 [6]. and LLaMA 3 77. respectively, resulting in sig- nificant improvements across various metrics while keeping the model architecture and infer- ence cost largely unchanged. Similarly, we aim to improve the acceptance rate and acceleration ratio of EAGLE by increasing its training data. Unfortunately, we observe that the gains from additional training data for EAGLE are limited. We analyze the reasons behind this phenomenon. As shown in the upper part of Figure 3 in the test
Figure 3: Illustration of training-ting time test (the bottom part) and its comparison with other draft methods (the upper and middle parts). denotes the feature, denotes the token, and a represents the unconstrained vectors. We use the hat to denote the predictions from models. For EAGLE and EA- GLE Iie. removal (the upper and middle parts), the training and test processes are different. How- ever, for EAGLE-3 (the bottom part), the training and test processes are the same.

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0.8
0.80
0.7
0.6
0.78
EAGLE
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EAGLE without fea pred
0.76
EAGLE-3
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0.3
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0.72
8
Figure 4: Comparison of acceptance rates across different methods, with the x-axis representing the data scale relative to ShareGPT.
phase, EAGLE performs autoregressive prediction at the feature level (as both input and output of the draft model are with hat), predicting the next feature and then feeding the feature into the LM head of the target model to obtain the token distribution. In the training phase, EAGLE's s loss function consists of two components: the feature prediction loss lea and the token prediction loss hoken- Thanks to the feature prediction loss, the draft model after training (when the input of the draft model is without hat and the output of the draft model is with hat) can adapt to the test case and acquire multi-step prediction capabilities. However, with token prediction as the ultimate goal, feature prediction can be seen as an additional constraint, which limits the expressiveness of the draft model and makes it difficult to benefit from increased data. After removing the feature constraint and expanding the training data (the middle part of Figure 3 as shown in Figure the acceptance rate O-cr of the first draft token improves significantly. However, the output of the draft model in the training phase, denoted as á is far away from the ground-truth fi+1, causing the input sequence fi, f2. fi, d++1 in the test phase to deviate significantly from the training distribution, resulting in a very low acceptance rate 1-a for the second draft token, as shown in Figure We can address this issue by moving the output d back into the input of draft model in the training process (the bottom of Figure 3). similar to autoregressive inference. Using this method, the benefits of increasing training data become more pronounced. We name this technique training-time test.
EAGLE and speculative sampling methods such as Medusa reuse the top-layer features of the target model, specifically the features immediately before the LM head. For LM head with full-rank weight matrix, the top-layer features corresponding to the logits of the next token are unique, ensuring that the information contained in these features aligns directly with the logits of the next token. However, predicting the next-next token based solely on top-layer features-whi which are inherently limited to the next token poses significant challenge. Fortunately, the training-time test technique described above enables the use of features from intermediate layers instead of relying solely the top layer, as the feature prediction loss leea has been removed during training.
This paper introduces EAGLE-3, an enhanced version of EAGLE that achieves a significant speedup:
A training-time test architecture for the draft model: constraint and directly predict tokens while simulating multi-step generation during training. This direct token prediction provides complete flexibility in the draft model's input. Instead of reusing only the top-layer features, we integrate and leverage low-, mid-, and high-level features from the target model, capturing rich semantic information from different layers.
We remove the feature prediction
A new scaling law for inference acceleration in LLMs: observe that increasing the amount of training data for the draft model leads to proportional increase in the speedup ratio of EAGLE-3. This scaling behavior was not observed in the original EAGLE architecture, as shown in Figure
With the new architecture,
Improved inference acceleration: EAGLE-3, trained with approximately 8x more data than EAGLE, achieves a 1.4x latency speedup over EAGLE-2 at batch size 1. Speculative sampling is often thought to reduce throughput at large batch sizes. However, in SGLang [ a production-gre framework, EAGLE-3 improves throughput by 38% at a batch size of 64. We expect larger data size would lead to further improved speedup ratio.
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2 Preliminaries
2.1 Speculative Sampling
Speculative sampling [1 12 L3 is lossless LLM acceleration technique that alternates between drafting and verification, where drafting is performed at low cost and verification is parallelized, corresponding the generation of drafts and the verification process, respectively. We use t, to denote the i-th token and Tash to represent the token sequence la; b, lb. When Tig is used as the prefix, the two stages of speculative sampling are as follows.
In the drafting stage, speculative sampling utilizes a draft model (a smaller version from the same series as the target model) to autoregressively generate k tokens to form the draft. Tz+1:j+k, while also recording the probability P for each token.
In the verification stage, speculative sampling invokes the target model to evaluate the draft T 75+1-j+ and records its probability p. Speculative sampling then determines the acceptance of draft to- kens sequentially, from front to back. For token fs+s. the probability of acceptance is given by min(1, Pj wise, a token is sampled from the distribution norm norm(max(0. Pi+s f)++)) to replace f and the remaining tokens in the draft are discarded. Appendix A.I of [T0] proves that speculative sampling is consistent with the distribution of vanilla autoregressive decoding.
If the token is accepted, the process moves to the next token. Other-
2.2 EAGLE and EAGLE-2
The draft model with limited capacity struggles precisely approximate the large-scale target model. EAGLE leverages the top-layer features of the target model as additional information and performs autoregression the feature level, simplifying the drafting process. EAGLE performs autoregression at the feature level and then uses the LM head of the target model to obtain the draft token. Due to the sampling results at the token layer being hidden, feature-level autoregression introduces uncertainty. EAGLE addresses this issue by feeding the token sequence from the previous time step, i.e., the sampling results, into the draft model. Unlike the chain-like drafts of Vanilla speculative sampling, EAGLE generates multiple draft tokens at the same position, resulting in tree-like draft. In the verification stage, EAGLE uses tree attention to parallelize the verification of the draft tree. Interestingly, EAGLE inspired the multi-token prediction technique used in the pre-training of D v3 [14, which in turn inspired new architectural designs in EAGLE-3.
EAGLE [2] and Medusa [8) among others, use thae-shaped drafts, where the structure of the draft tree predefined, static, and context-indepe tnntexxtindependent The difficulty of drafting closely related to the context, and a static draft tree can lead to resource wastage. EAGLE-2 [15] approximates the acceptance rate using the confidence of the draft model and dynamically generates the draft tree based on this, performing pruning of the draft tree at the end of the drafting stage. EAGLE-3 also adopts the context-awe -aware dynamic draft tree proposed in EAGLE-2.
3 EAGLE-3
In this section, we provide a detailed description of the implementation of EAGLE-3.
3.1 Inference Pipeline
Consistent with other speculative sampling methods, EAGLE-3 alternates between the drafting and verification stages. The difference between EAGLE-3 and EAGLE lies in the drafting stage, which we introduce with example, shown in Figure 5 Consider the prefix "How can".. During the prefill phase or the previous verification stage, the target model performs a forward pass to generate the next token, "T". We record the low, middle, and high-level feature sequences from the target model's 's forward pass, denoted as I, m, and h, respectively. We concatenate the k-dimensional vectors 1, m, and h to form 3i-dimensional vector, then pass it through fully connected (FC) layer to reduce it to k-dimensions, obtaining a feature g that integrates information from different layers. Here, i refers to the hidden size of the target model.
Our goal is to generate draft token sequence with the prefix "How can I". By inputting only Show and Ican. the draft model cannot access the random sampling process. Therefore, similar to EAGLE (2).

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Figure 6: Diagram of the attention causal masks during training- test. It sequentially shows native training step (the first step) and two simulated training steps (the second and third steps). The arrows between tokens represent contextual relationships. The gray tokens represent the training data while the blue and yellow tokens represent the first- and second-round predictions by the draft model, respectively. We use the training dataset as the labels for each token position.
introduce the embedding e₁ of the sampled token "T". The concatenated vector is then passed through an FC layer to reduce its dimensionality to k, and subsequently inputted into a single layer decoder, producing the output a. Finally, input or into the LM head and sample to obtain the draft token "do".
In Step I, with the prefix "How can", reuse Show and Yean from the target model. In Step 2. the prefix becomes "How can I". Ideally, we would reuse 9how. Ican- and 91 from the target model. However, this is not possible because the token "T" has not yet been checked by the target model, and cannot obtain şr- Instead, we use the output aj from the draft model in the previous step to replace Øt and concatenate di with the embedding 6do of the sampled result "do" the input the draft model in Step In Step 3, we similarly cannot obtain Ido. so Odo replacement, concatenating ddo with en as the input to the draft model. The same approach is followed for subsequent steps.
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3.2 Draft Model Training
do
The input to the draft model in EAGLE is ei- ther, or least approximately, the top-layer fea- tures J1, f2.2..... Se of the target model. In con- trast, the input to the draft model EAGLE- 3 may include the features 91, 92. ... 99t from the target model, or may include the out- put a++1, At+2 ae+j from the draft model. Therefore, we need to train the draft model to adapt to different inputs. During training, perform test steps, where we generate a and feed back into the draft model for further training-
khow
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Figure Diagram of the EAGLE-3 inference pipeline. m, and h represent the low, middle, and high-level features the target model, respec- tively. e denotes the embedding.
The core of the draft model in EAGLE-3 Transformer decoder layer. Aside from the self-
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attention operation, no other components interact with the context, so no further modifications are required during training or testing. The only component that requires slight modification is the self-attention, which we will describe in detail below.
Although the actual input consists of features, for clarity, we describe the process using tokens as input. As shown in Figure the original training data is a sequence of length 3, "How can I", with normal sequential dependency in the context. Therefore, the attention mask is standard lower triangular matrix. The outputs at the three positions are "are", "we", and "do", which have a tree-like contextual relationship with "how", "can", and 'T". As a result, when the input "are", "we", and "do" is fed into Step 2. the attention mask needs to be adjusted accordingly. as shown in the top-right corner of Figure O All attention masks are diagonal, except when the original training data is used as the key. Using matrix multiplication in this case would result in significant computational waste, so we can use vector dot products to calculate the attention score only for the corresponding positions.
HASS 3 and EAGLE-3 both make similar modifications to the attention mechanism to simulate the testing process during training, but this is not the main focus of EAGLE-3, The motivations, methods, and outcomes of the two approaches are distinctly different. The motivation behind HASS mitigate the error accumulation caused by inaccurate feature predictions in EAGLE. HASS still performs feature prediction, includes a feature prediction loss Leca. and the input to the draft model must be the top-layer features. In contrast, the motivation behind EAGLE-3 is to remove unnecessary constraints to enhance the model's 's expressive power. EAGLE-3 no longer requires the draft model's output to fit the top-layer features of the target model, thus avoiding error accumulation. After removing feature prediction, the input to EAGLE-3 is completely free, and it is replaced by a fusion of features from different layers of semantic information. The removal of the feature prediction loss also enables us to discover new scaling law for inference acceleration which was never found before. Figure D and Table also shows the speedup of EAGLE-3 and HASS, with EAGLE-3 demonstrating significantly better performance.
Experiments
Models. We conduct experiments with open- open-source chat and reasoning models, including Vicuna 13B [6]. LLaMA- 3.1 8B, ALLMA-Inntruee 3.3 70B (7]. and -----sel-----isil-LLLL Due to the GPU constraint, we are unable to test EAGLE-3 on models larger than 70B.
8B 17.
Draft Models. Same to EAGLE and EAGLE-2, the draft model of EAGLE-3 consists of a single transformer layer. So, the scale of draft models in EAGLE, EAGLE-2, and EAGLE-3 is nearly the same.
Tasks. Following EAGLE (2) and Spec-Bench 18] we evaluate on five tasks, using the same weights for all tasks without fine-tuning on the respective tasks. For multi-turn conversation, code generation, mathematical reasoning, instruction following, and summarization, we chose the MT-bench [19],
HumanEval 20). GSMSK (11,, Alpaca [22], and CNN/Daily Mail 23 datasets, respectively.
Metrics. EAGLE-3 does not modify the target model's weights and uses strict speculative sampling acceptance conditions, ensuring no loss in performance. Therefore, we do not evaluate generation quality. Instead, we use the following metrics to assess the acceleration performance:
Speedup Ratio:
The actual test speedup ratio relative to vanilla autoregressive decoding.
Average Acceptance Length :: The average number of tokens generated per drafting- verification cycle, which corresponds to the number of tokens accepted from the draft.
Acceptance Rate π-a: The proportion of draft tokens accepted, which directly reflects the draft model's approximation to the target model. Following EAGLE's setup, we use chain-like draft rather than a tree-like draft when testing acceptance rates. EAGLE suffers from error accumulation, meaning that the input to the draft model may be its own estimates rather than the exact values from the target model. Therefore, EAGLE uses n-a to represent the acceptance rate when the input contains estimated features, under the condition that the previous estimated tokens are all accepted by the target model. In other words, the acceptance rate for inputs fI, f2, fc. fit1.-.. -.. futn where f is the exact value and f is the draft model's estimate. Similarly, we use n-a to represent the acceptance rate in EAGLE-3 when the input contains self- ceff-predicted values a, i.e., the acceptance rate for inputs 91, $2.92."..
99 0.+1, attns where g is the fused feature from the target model.

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Table I: Speedup ratios and average acceptance lengths T of different methods on A100 GPUs. V represents Vicuna, L31 represents LLaMA-Instrut 3.1, L33 represents LLaMA-Instruct 3.3, and DSL represents
SpS denotes standard speculative sampling, with its draft model being Vicuna-68M. Methods like Medusa relax acceptance conditions under non-greedy settings, which do guarantee lossless acceleration. Therefore, we do compare EAGLE-3 with these methods when temperature=l.
MT hench
HumanEEval
GSMSK
Alpaca
CNN/DM
Mean
Model
Method
Speedup
Speedup
Speedup
7
Speedup
Speedup
Speedup
T
Temperature
SpS
1.93x
2.27
2.23x
2.57
1.77x
2.01
1.76x
2.03
1.93x
2.33
1.92x
2.24
1.58x
1.93
1.16x
2.42x
2.50
Medusa
2.07x
2.59
2.50x
2.78
2.23x
2.64
2.08x
2.45
1.71x
2.09
2.12x
2.51
Lookahead
1.65x
1.75
1.46x
1.46x
1.50
1.67
Hydra
2.88x
3.65
3,28x
3.87
2.93x
3.66
2.86x
3.53
2.05x
2.81
2.80x
3.50
EAGLE
3.58x
3.97
3.03x
2.49x
3.96
EAGLE-2
4.26x
4.83
4.96x
5.41
4.22x
4.79
4.25x
4.89
3.40x
4.21
4.22x
4.83
EAGLB-3
6.47x
7.54
5.16x
5.01x
EAGLE-2
3.16x
4.05
3,66x
4.71
3.39x
4.24
3.28x
4.12
2.65x
3.45
3.23x
4.11
1 8B
HASS
3.55x
4.41
3,78x
4.85
3,45x
4.47
3,57x
4.55
2.77x
3.55
3.42x
4.37
EAGLE-3
4,40x
6.13
4.85x
6.74
4.48x
6,23
4.82x
6.70
3.65x
5,34
4.44x
6.23
EAGLE-2
2.83x
3.67
3.12x
4.09
2.83x
3.69
3.03x
3.92
2.44x
3.55
2.85x
3.78
L33
EAGLE-3
4.11x
4.79x
6.52
4.34x
6.15
4.30x
3.27x
5.02
4.12x
5.88
EAGLB-2
2.92x
3.80
3,42x
4.29
3.40x
4.40
3.01x
3,80
3.53x
3.33
3.26x
3.92
DSL
RAGLE33
4.05x
6.38
6.93
3.65x
3.52x
5,84
Temperature=1
SpS
L.62x
1.84
1.72x
1.97
1.46x
1.73
1.52x
1.78
1.66x
1.89
1.60x
1.84
BAGLE
2.32x
3.63
3.60
2.23%
3.26
2.44x
3.45
V 13B
EAGLE-2
3.80x
4.40
4.22x
4.89
3,77x
4.41
3.78x
4.37
3.25x
3.97
3.76x
4.41
EAGLE-3
4.57%
5.42
5.15x
6.22
4.71x
5.58
4.49%
5.39
4.33x
5.72
4.65x
5.67
RAGLE-2
2.44x
3.16
3.39x
4.39
2.86x
3.74
2,83x
3.65
2.44x
3.14
2.80x
3.62
1,31 8B
HASS
2.58x
3,48x
4.50
2.87x
3.77
3.04x
2.42x
3.11
2.89x
3.73
EAGLES
3.07%
4.24
4.13x
5.82
3.32x
4.59
3.90x
5.56
2.99x
4.39
3.45x
4.92
EAGLE-2
2.73x
3.51
2.89x
3.81
2.52x
3.36
2.77x
3,73
2.32x
3.27
2.65x
3.54
L33 70B
EAGLE-3
3.96x
5.45
4.36x
6.16
4.17x
5.95
4.14x
5.87
3.11x
4.88
3.95x
5.66
EAGLB-2
2.69%
3.41
3.01x
3.82
3.16x
4.05
2.6-X
3,29
2.35x
3.13
2.77x
3.54
DSL. 8B
EAGLE-3
3.20x
4.49
3.77x
5.28
4.38x
6.10
3.16x
3.08x
4.27
3.52x
4.89
Implementation. We use the A optimizer, with beta values (81. 32) set to (0.9, 0.95) and implemented gradient clipping of 0.5. The learning rate set to 5e-5. We simulate 5 steps during training- time test. We use ShareGPT and UlrraChat-200K (24) as training data, containing approxi- mately 68K and 464K data entries, respectively. We call the target model generate responses rather than using a fixed dataset. For the reasoning model DeepSeek- R I--istill-LLaMMe 8B, we also use the OpenThoughts- 1 14k-math dataset for training. We use 16x A100 GPUs for the training of EAGLE-3 head for 70B models in two weeks. If not specified, we use the A100 GPU to test 70B models and the RTX 3090 for other models. The testing environment for methods accelerating the same target model is identical.
Comparison. We use vanilla autoregressive decoding the baseline, which serves the benchmark for speedup ratios (1.00x). We compare EAGLE-3 with recent lossless speculative sampling methods, including standard speculative sampling [10 25), PLD (26), Medusa (8), Lookahead [27), Hydra
(88) HASS 3 EAGLE (2). and EAGLE-2 15
4.1 Effectiveness
Figure D and Table T demonstrate the acceleration performance of EAGLE-3, On all tasks and target models, EAGLE-3 achieves the highest speedup ratio and average acceptance length. EAGLE-3 provides a speedup of approximately 3.0x-6.5 6.5x compared vanilla autoregressive generation, with 20%-40% improvement over EAGLE-2. Different tasks affect the draft model's acceptance rate, so both the average acceptance length and speedup ratio are task-dependent. Due to the presence of many fixed templates in code generation tasks, generating drafts is the easiest, which is why EAGLE-3 performs best on HumanFyal, achieving a speedup ratio of up to 6.5x and an average 8B is exception, with the highest
acceptance length of up 7.5. CrepSeek-RI-Distil---
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observed in the previous speedup ratio on the mathematical reasoning dataset GSM8K. This may be because we trained the draft model of DeepSeek RI-Distill Distill-LLaMA 8B using the OpenThoughts-11 14k-math dataset.
Figure 7 shows the acceptance rates of EAGLE and EAGLE-3 on MT-bench with LI LaMt--nstruut 3.1 8B the target model. The acceptance rate of EAGLE-3 is significantly higher than that of EAGLE. As the imput from the draft model itself increases, the acceptance rate of EAGLE drops significantly, whereas EAGLE-3's acceptance rate remains almost unchanged, demonstrating the effectiveness of the Training-time test.
0.80
0.75
Accept rate
0.70
EAGLE
0.65
EAGLE-3
0.60
0.55
0.50
0-a
1-a
2-a
3-a
4-a
5-a
7-x
Figure 7: Acceptance rate of EAGLE and EAGLE-3 on MT-beech, with the target model being LLaMA-Instructt, 3.1 8B. Hereby, n-a refers to the acceptance rate when the imput contains estimated features, under the condition that the previous estimated tokens are all accepted by the target model.
4.2 Ablation Study
The improvements of EAGLE-3 mainly come from three aspects: first, the removal of the feature regression constraint, second, the improvement from reusing only the top-layer features to reusing mix low, middle, and high-level features, and third, the increase of training data. We conducted an ablation study MT-bench with LLaMA-Instrucc 3.1 8B the target model. The results, shown in Table 2 indicate that the first and second improvements in EAGLE-3 significantly enhance the acceptance length and speedup ratio, demonstrating the rationality of the EAGLE-3 design. Figure shows how the speedup ratio increases w.r.t. the amount of training data. The new architectural designs in EAGLE-3 enable an increasing scaling curve, which works.
Table Ablation study results with LLaMA-Instruct 3.1 8B as the target model. "Remove fea con" refers to the first improvement of EAGLE-3, which removes the feature prediction constraint, "Fused features" " refers to the second improvement of EAGLE-3, where low, middle, and high-level feature fusion replaces the of top-layer features.
MT-bench
GSM8K
Method
Speedup
T
Speedup
EAGLE-2
3.16x
4.05
3.39x
4.24
+ remove fea con
3.82x
5.37
3.77x
5.22
fused features (ours)
4.40x
6.13
4.48x
6.23
4.3 EAGLE-3 in SGLang
Speculative sampling algorithms reduce memory accesses and lower latency during memory- bound decoding by leveraging redundant computational power. As batch sizes increase, this redundancy decreases, reducing the effectiveness of speculative sampling. Efficiency improvements are more challenging in highly optimized production-gran grade frameworks. The performance of EAGLE-3 for large batches on a single H100 GPU and LLLMA-Instruet 3.1
the SGLang v0.4.4 environ- ment 99 was evaluated in Table 3 This part of the experiment did not use the tree structure, the chain length was set 3, and the testing dataset was MT-Bench. EAGLE reduces throughput at batch size of 24, whereas EAGLE-3 still achieves a 38% throughput improvement at a batch size of 64.

---

Table Throughput improvement under different batch sizes on H100 and LLaMA-Instrue 3.1 8B for the MT-Bench dataset, with SGLang without speculative sampling as the baseline (1.00x).
Batch size
8
16
24
32
48
56
EAGLE
1.40x
1.38x
1.23x
1.02x
0.93x
0.94x
0.88x
0.99x
0.99x
EAGLE-3
1.81x
1.82x
1.62x
1.48x
1.39x
1.32x
1.38x
1.34x
1.38x
We also tested the throughput of EAGLE-3 batch size = on H100 when the target model LLaMA-Instruct 3.1 8B and the testing dataset is MT-bench. The results shown in Table
Table 4: Throughput at batch size 1 on a single H100 GPU when the target model is Il-strutttuutt 3.1 8B and the testing dataset is MT-bench.
Method
Throughput (bs=1)
SGLang (w/o speculative, 1x H100)
158.34 tokens/s
SGLang EAGLE-2 (Ix H100)
244.10 tokens/s
SGLang EAGLE-3 (Ix H100)
373.25 tokens/s
4.4 EAGLE-3 in vLLM
We also conducted a study on the impact of EAGLE-3 on throughput for large batch sizes based on vLLM [29], widely used production-gres -gadde framework, and the results RTX3090 and LLaMA- Instruct 3.1 8B are shown in Table 5 EAGLE shows the maximum throughput improvement at a batch size of 24, while EAGLE-3 shows this 56. This part of the experiment did not use the tree structure, the maximum chain length was set to 2, and the testing dataset was MT--Bech.
Table 5: Throughput improvement under different batch sizes on RTX3090 and LLaMA-Instruct 3.1 8B for the MT- MT-Bench dataset, with vLLM without speculative sampling as the bascline (1.00x).
Batch size
2
4
8
16
24
32
48
56
EAGLE
1.30x
1.25x
1.21x
1.10x
1.03x
0.93x
0.82x
0.71x
EAGLE-3
1.75x
1.68x
1.58x
1,49x
1.42x
1.36x
1.21x
1.01x
5
Related Work
Many methods have been used accelcrate inference in LLMs, such quantization 30 34 and distillation (35). These methods generally have trade-offs, where there is a need to balance model performance with acceleration benefits.
3
32
33
Speculative sampling uses the target model for verification ensure lossless acceleration. Early speculative decoding methods [36, 37) accelerated generation in greedy settings, while [10 [1]
introduced speculative sampling to extend the draft verification framework to non-greedy generation.
Many subsequent works have improved upon speculative sampling. EAGLE 22, EAGLE-2 [15]
Medusa (8), and Hydra (28) reused the features of the target model. Based on the framework of
EAGLE, HASS 3 simulates multistep draft process during training mitigate the issues of training-inference inconsistency and error accumulation EAGLE, GLIDE and CAPE [38] reuse the target model's KV cache, while methods [39 40
45 46 47 like Draft & Verify
48
use layer skipping or early exits to reuse parts of the target model's parameters.
There are several key differences between HASS 3 and EAGLE-3. First, HASS drafts using only top-layer features, whereas EAGLE-3 integrates low-, mid- and high-level features. Second, HASS retains the token loss hooen. while EAGLE-3 removes it to improve model capacity. Third, unlike HASS, EAGLE-3 exhibits clear scaling law trend. Finally, EAGLE-3 significantly outperforms HASS, demonstrated in Figure and Table I

---

Conclusion
In this paper, we introduce EAGLE-3. Building upon EAGLE, EAGLE-3 incorporates two key improvements. First, it removes the feature prediction constraint, instead directly predicting draft tokens through a Training-time test. Second, it replaces the use of the target model's top-layer features with a fusion of the target model's lower, middle, and upper-layer features to obtain richer information. With these improvements, EAGLE-3 continues benefit from the augmentation of training data, achieving maximum speedup of 6.5x.
Acknowledgement
We would like to thank James Liu, Ke Bao, Yineng Zhang, Lianmin Zheng, Ying Sheng, and many others in the SGLang team for evaluating EAGLE-3 in the SGLang environment. Hongyang Zhang supported by the NSERC Discovery Grant RGPIN- 2022-03215, DGECR-2022 2022-00377 and Google Research Scholar Award.
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12

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A Implementation Details
Vanilla:
We use models from the Huggingface. Huggingfaae..ranssoome transformers library with the PyTorch backend and pre-allocated KV cache. Other methods also use these models as their base.
(Standard) Speculative Sampling: We use the assisted generation feature from the HuggingFace Transformers library.
PLD, Lookahead, Medusa, and Hydra: We use the default settings and the officially released weights.
EAGLE:
Vicuna and LLaMA2-Chal draft models use the officially released weights, while LLaMA3- Instruct is trained using the ShareGPT dataset (consistent with Medusa and Hydra).
EAGLE-2: For the 7B (8B), 13B, and 70B original LLMs, we set the total number of draft tokens to 60, 50, and 48, respectively, with draft tree depth of 6, and select 10 nodes during the expansion phase.
EAGLE-3: EAGLE-3's draft model achieves a significantly higher acceptance rate, allowing to increase draft tree depth from to while keeping the number nodes the same as EAGLE-2.
B A Comparative Study of EAGLE-3 and HASS
The work most similar to EAGLE-3 is HASS. Both approaches simulate multi-step prediction during training, but this is neither the main focus of EAGLE-3 nor HASS. Training-time testing primarily involves adjusting the attention mask enforce correct dependencies, which essentially simplifies tree attention into a fixed-shape form (as illustrated in Figure In fact, tree attention has been widely adopted in nearly all speculative decoding methods proposed in recent years. Feeding model outputs instead of ground truth during training, known as scheduled sampling, was also widely explored in the RNN era.
The core contribution of HASS lies in identifying the train-test mismatch EAGLE and mitigating it through tree nateetion-bbsed simulation. In contrast, EAGLE-3 focuses different issue: the inability of EAGLE to benefit from data scaling. EAGLE-3 attributes this limitation to the feature prediction constraint-aa issue also present in HASS. EAGLE-3 removes this constraint and uses tree attention for simulation. This modification enables EAGLE-3 to scale effectively with increased training data, whereas HASS does not. The ability scale with data is the contribution of EAGLE-3.
Figure 8 illustrates the performance of EAGLE-3 and HASS across different training data scales. Sim- ilar to EAGLE-2, HASS fails to scale up. while EAGLE-3 exhibits rapid performance improvements as more training data becomes available. Moreover, EAGLE-3 identifies that the top-layer features (used in EAGLE and subsequent works including HASS) tend to overfit to next-token prediction and not well-suited for multi-ste step draft generation. To address this, EAGLE-3 replaces the top-layer features with a fusion of multi-level features. Therefore, EAGLE-3 also outperforms HASS when trained smaller datasets.
4.4
EAGLE-2
6.0
EAGLE-2
4.2
EAGLE-3
Accept length
EAGLE-3
94.0
HASS
5.5
HASS
Speedup
3.8
0..0
5.0
3.6
3.4
4.55
3.2
4.0
1
8
Data scale
Data scale
Figure 8: Scaling law evaluated on the MT-bench using LLaMA--nstruse 3.1 8B as the target model, with the x-axis representing the data scale relative to ShareGPT.
13

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Reviews:
[{"review_id": "hCT2ZTbwbz", "reviewer": "Reviewer_o9ZR", "summary": "In this manuscript, the authors propose a novel speculative decoding approach named EAGLE-3. The draft model performs autoregressive generation within an independent latent state space, enabling fast and accurate prediction of subsequent tokens. To enhance the informativeness of these predictions, the method incorporates hidden state fusion, allowing the draft model to access richer contextual representations. This combination endows the draft model with greater potential capacity, which is further unlocked by scaling up training. The proposed method is evaluated across various models, achieving promising results. Finally, the authors port their approach to different inference frameworks to examine its potential under batched decoding scenarios.", "questions": "1. Could the authors provide detailed specifications regarding the model and training configurations used in the study?\n\n2. Could the authors explain the rationale behind the specific design choices adopted in the inference framework? Additionally, it would be helpful to further elaborate on the compatibility of EAGLE-3 with these deployment frameworks.\n\n3. While training time may not be the most critical factor, the reported two-week training using 16 A100 GPUs appears to raise the practical barrier to adoption. Could the authors offer further explanation or justification for this level of computational demand?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n1. The proposed fusion of hidden state information and the removal of the constraint loss significantly enhance the learning capacity of the draft model. This provides new insights into how to effectively train draft models.\n\n2. The authors conduct training across models of various sizes, demonstrating the robustness and scalability of EAGLE-3 under different base model configurations.\n\n3. Despite space limitations, the authors port their implementation to practical deployment frameworks and discuss the acceleration potential under realistic conditions. This brings the evaluation closer to production-level settings.\n\n**Weaknesses:**\n\n1. While the paper mentions the fusion of three hidden layers (denoted as $l$, $m$, and $h$), it provides no details on how these specific layers are selected. Nor does it discuss how different choices might impact performance, such as whether three layers are optimal compared to using two or four. This lack of clarity obscures the model architecture and leaves readers without sufficient information or confidence to reproduce the reported results.\n\n2. The authors do not appear to specify the number of sampling steps used during training, which also undermines the reproducibility of the method.\n\n3. Although migrating the implementation to established inference frameworks is a commendable step, the paper lacks a detailed justification for the chosen configuration, specifically, the chain length of 3 and the decision to omit the draft tree. Moreover, we observe that even with a batch size of 1 (contrary to the large-batch setting emphasized in the paper), the reported speedup on H100 using *sglang* falls significantly short of earlier results. This discrepancy suggests that the earlier performance gains may have limited applicability in real-world scenarios. As such, the proposed method has yet to convincingly demonstrate its suitability for large-scale deployment, rather than being merely a toy-scale solution.\n\n4. The authors report utilizing approximately 5,000 A100 GPU hours to train the 70B model. However, based on the parameter partitioning strategy described, where full backpropagation is not required, the computational workload appears to be only slightly greater than that of inference and significantly less than that of full model training. The reported training duration thus raises questions and calls for further clarification.", "was_revised": true, "final_justification": "**Addressed Issues**\n- The authors adequately addressed the first concern by providing additional experiments and clarifications.\n- For the second concern, the authors provided a thorough and well-reasoned analysis that helps clarify the root cause, even though a complete solution has not yet been achieved.", "rebuttal": "Thank you for your time and effort in reviewing our manuscript. We sincerely appreciate your valuable comments and suggestions. Below, we provide detailed responses to your concerns.\n\n> Q1. While the paper mentions the fusion of three hidden layers (denoted as $l$, $m$, and $h$), it provides no details on how these specific layers are selected.\n\n\nA1. We select the 3rd, $n//2$-th, and $(n{-}3)$-th layers, where $n$ is the total number of layers. The table below presents experimental results on different layer selections. As long as the final layer is not used and the features are fused, the performance improves significantly, while the specific choice of layers has limited impact. Furthermore, increasing the number of layers beyond three does not bring additional gains. Therefore, we opt for the $3$-rd, $n//2$-th, and $(n{-}3)$-th layers in our design. The evaluation is conducted on the MT-Bench dataset, with LLaMA-Instruct 3.1 8B as the target model.\n\n| Layers | Accept Length    | Speedup    |\n|------------|------|------|\n| $n$      | 5.37 | 3.82x | \n| $n-1$    | 5.65 | 4.00x | \n| $n-2$    | 5.76 | 4.06× | \n| $n-3$    | 5.76 | 4.05x | \n| $n-3$, $n−3$    | 5.93 | 4.26x | \n| $n-3$, $n//2$    | 6.02 | 4.31x | \n| $3$, $n//2$, $n−3$   | 6.13 | 4.40× | \n| $1$, $n//2$, $n−1$   | 6.11 | 4.38× |\n| $5$, $n//2$, $n−5$   | 6.09 | 4.37× |\n| $3$, $n//2-5$, $n//2+5$, $n−3$   | 6.11 | 4.37× |\n| $3$, $10$, $n//2$, $n-10$, $n−3$   | 6.15 | 4.39× |\n\n> Q2. The authors do not appear to specify the number of sampling steps used during training, which also undermines the reproducibility of the method.\n\n\n\nA2. Thank you for the reminder. We used 7 sampling steps during training, and we will clarify this detail in the next revision.\n\n> Q3. Although migrating the implementation to established inference frameworks is a commendable step, the paper lacks a detailed justification for the chosen configuration, specifically, the chain length of 3 and the decision to omit the draft tree.\n\n\n\n\nA3. Speculative sampling utilizes the redundant computational capacity of GPUs. However, under the high batch size setting targeted by SGLang, such redundancy becomes limited. Consequently, we disable the computationally intensive tree-based drafting. Moreover, longer speculative chains require more resources, and our experiments show that a chain length of 3 achieves the best performance across most batch sizes. Therefore, we adopt this configuration in all our experiments.\n\n> Q4. Moreover, we observe that even with a batch size of 1 (contrary to the large-batch setting emphasized in the paper), the reported speedup on H100 using sglang falls significantly short of earlier results. This discrepancy suggests that the earlier performance gains may have limited applicability in real-world scenarios. As such, the proposed method has yet to convincingly demonstrate its suitability for large-scale deployment, rather than being merely a toy-scale solution.\n\nA4. On fixed hardware, all acceleration techniques and engineering optimizations aim to approach the hardware’s performance limits. Naturally, SGLang—already equipped with many optimizations—is more difficult to accelerate further compared to an unoptimized Huggingface setup. It is widely acknowledged in the community that production environments are harder to accelerate than experimental ones, and this applies to all acceleration methods. Accordingly, even modest improvements in production settings directly translate to cost savings.\n\nSGLang exemplifies the type of large-scale deployment scenario you referred to. Our experiments show that EAGLE-3 performs effectively in this environment, achieving a 2–3× reduction in latency compared to vanilla inference on SGLang. This highlights that our method extends well beyond toy-scale applications. Moreover, EAGLE-3 has been **officially integrated into large-scale inference frameworks such as SGLang and vLLM**, further demonstrating its practicality and scalability in real-world production systems.\n\n> Q5. However, based on the parameter partitioning strategy described, where full backpropagation is not required, the computational workload appears to be only slightly greater than that of inference and significantly less than that of full model training. The reported training duration thus raises questions and calls for further clarification.\n\n\nA5. Although EAGLE-3 uses only a single layer, it requires 7 sampling-training cycles, making the overall training cost relatively high (roughly equivalent to training 7 layers in a 70B model with 80 layers in total). Moreover, since our focus is on inference efficiency, we did not apply specific optimizations to the training process. In fact, organizations that require the deployment of 70B models typically have access to abundant computational resources, making this level of overhead acceptable."}, {"review_id": "LpJmrvoSrk", "reviewer": "Reviewer_YVao", "summary": "This work presents EAGLE-3, which introduces a novel training strategy called training-time test, where the draft model simulates multi-step generation during training. This improves alignment between training and inference. Unlike the original EAGLE that predicts the next feature, EAGLE-3 performs token-level drafting, directly predicting tokens. This eliminates the need for feature prediction loss and allows for more expressive draft models. It also employs multi-layer feature fusion, leveraging features from low, middle, and high layers of the target model to provide richer semantic context. Moreover, EAGLE-3 is scalable with more training data for speculative decoding.\n\nIn the benchmark, EAGLE-3 achieves up to 6.5× speedup over standard autoregressive decoding and is 1.4× faster than EAGLE-2. When deployed in SGLang, it delivers a 1.38× throughput improvement at batch size 64, highlighting its practical efficiency. This is an impactful and strong work with open source project integration.", "questions": "none", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "strength: \n1. address an important question - we always want to reduce the cost of inference of LLMs\n2. significant inference speedup and production impact\n - achieves up to 6.5x speedup, outperforming all previous speculative sampling methods including EAGLE-2, HASS, and Medusa.\n - evaluated in SGLang and vLLM, EAGLE-3 delivers real throughput improvements (e.g., 1.38x at batch size 64), showing real-world viability.\n3. data scaling behavior\n - the paper introduces a scaling law: as training data increases, performance continues to improve, which was absent in previous spec decode work.\n4. Training-time test strategy\n - Aligns the training and inference pipelines by simulating multi-step generation during training.\n - Reduces distribution shift between training and deployment, mitigating error accumulation. It's done via removing feature level predicition, and fusing low/mid/high level features to predict tne token\n\nweakness:\n1. limited model architecture coverage, MoE is becoming the mainstream backbone and getting more important\n2. data scaling means that training the draft model becomes more expensive (but if the inference cost still dominates, that's fine. It's just not friendly to labs)\n3. limited applicability for proprietary models where internal features cannot be accessed, such as openai gpt or claude models", "was_revised": true, "final_justification": "Concerns addressed", "rebuttal": "Thank you for your thoughtful review and constructive feedback. We truly value your input and have addressed your concerns in the following responses.\n\n> Q1. Limited model architecture coverage, MoE is becoming the mainstream backbone and getting more important.\n\n\nA1. EAGLE-3 is also applicable to accelerating MoE models. The table below shows the acceleration performance on LLaMA4 Scout (MoE, 109B) and LLaMA4 Maverick (MoE, 400B) within the SGLang framework. It is important to note that results obtained in the production-grade SGLang framework should not be directly compared with those from standard Huggingface-based setups.\n\n| Model | Llama 4 Scout (MoE, 109B)   | Llama 4 Maverick (MoE, 400B)   |\n|------------|------|------|\n| Speedup      | 2.00x | 2.18x | \n\n> Q2. Limited applicability for proprietary models where internal features cannot be accessed, such as openai gpt or claude models.\n\nA2. OpenAI and Anthropic can train EAGLE-3 heads for their proprietary models.\nFor those models, EAGLE-3 can reduce deployment costs for companies by improving generation speed. This, in turn, lowers the cost for end users and enhances the overall user experience."}, {"review_id": "U3vKvpfMol", "reviewer": "Reviewer_9fyH", "summary": "This paper proposes EAGLE-3, a new speculative decoding framework for accelerating large language model (LLM) inference. The main contributions are: (1) Compared to previous EAGLE series, EAGLE-3 removes the feature prediction loss and retains only the token prediction loss, and introduces a training-time test mechanism so that the draft model learns to generate token sequences more likely to be accepted by the main model during training. (2) EAGLE-3 introduces multi-layer feature fusion, concatenating low-, mid-, and high-level hidden states and fusing them via a fully connected layer for draft model token prediction. Experiments on LLaMA, Vicuna, and other mainstream LLMs show up to 6.5x speedup without loss of generation quality, and demonstrate improved scaling law behavior compared to EAGLE-2.", "questions": "- **Can you provide a clear mathematical formulation for the multi-layer feature fusion mechanism?** Section 3.1 only gives a high-level description. Please specify the exact formula and rationale for the choice of layers.\n- **How are the low-, mid-, and high-level features selected?** Please clarify which layers are used and provide ablation studies for different layer combinations, not just with/without fusion (see Table 2).\n- **Can you provide a theoretical analysis for why multi-layer feature fusion improves draft model prediction?** This is important for understanding the generality and limitations of the approach.\n- **Is there any theoretical justification for the effectiveness of the training-time test mechanism on draft model generalization?** Please discuss or analyze its impact beyond empirical results.\n- **Can you provide controlled experiments where EAGLE-3 and baselines (e.g., HASS ) are trained with the same amount of data?** This would clarify the contribution of the method versus data scale.\n- **Do you plan to evaluate EAGLE-3 on larger or more diverse models (e.g., Qwen-32B or MoE)?** Please discuss the potential challenges and applicability. Also, will the scaling law also works on larger or more diverse models?", "limitations": "The authors discuss some limitations (Section 6), but do not sufficiently address the lack of theoretical analysis, the limited model coverage, or the fairness of experimental comparisons. I encourage the authors to be more explicit and thorough in discussing these points.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n- The method is novel and the experimental evaluation is extensive, covering multiple LLMs and tasks. The results are convincing and show significant speedup.\n- LLM inference acceleration is a highly impactful problem for both academia and industry. EAGLE-3 demonstrates strong practical value.\n- EAGLE-3 makes clear advances over the EAGLE series by removing feature prediction loss, introducing training-time test, and using multi-layer feature fusion. These design choices lead to better scaling and draft model performance.\n- The paper is well organized and generally easy to follow.\n\n**Weaknesses:**\n\n- The paper lacks theoretical analysis for its core innovations. Multi-layer feature fusion and training-time test are only motivated intuitively, with no mathematical formulation and theoretical justification. This is a major weakness for NeurIPS standards.\n- The implementation of multi-layer feature fusion is described only at a high level, with no clear mathematical formula and rationale for layer selection. The ablation study only compares with/without fusion, lacking analysis of different layer combinations.\n- Experiments are limited to LLaMA, Vicuna, and similar models (up to 70B). There is no evaluation on more recent or larger models (e.g., Qwen-32B or MoE), which limits claims of generality.\n- EAGLE-3 is trained with more data than baselines, which may confound the effect of the method with data scale. There is no controlled comparison under equal data conditions.\n- The discussion of limitations is brief and does not address the above theoretical and practical issues in depth.", "was_revised": true, "final_justification": "Thank you for the thorough rebuttal. I appreciate the effort you've put into addressing my concerns, but I'm still not convinced by the responses to Q1/Q2 and therefore will maintain my original score.\nFor Q1, while you provide mathematical formulas and ablation studies, the layer selection strategy lacks theoretical foundation. The fixed rule (3rd, n//2-th, n-3-th layers) appears to be empirically driven without explaining why these specific layers capture the most relevant information for speculative decoding. More critically, applying the same layer selection criteria across all model architectures is questionable—different models with varying depths and architectural designs may require adaptive strategies rather than a one-size-fits-all approach. The absence of cross-architectural validation and semantic analysis of selected layers weakens the scientific rigor of this core component.\nFor Q2, the evaluation on larger models remains insufficient. Testing only LLaMA4 variants doesn't demonstrate generalizability across diverse architectures (Qwen, Gemma, etc.). The acknowledged incomparability between SGLang and Huggingface results further undermines the evidence. Additionally, the notably lower speedups on MoE models (2.18x vs 4.40x on dense models) without analysis or explanation raises concerns about the method's scalability and robustness across different model types.", "rebuttal": "Thank you for your valuable suggestions. In what follows, I will address your questions and clarify some misunderstandings.\n\n> Q1. The implementation of multi-layer feature fusion is described only at a high level, with no clear mathematical formula and rationale for layer selection. The ablation study only compares with/without fusion, lacking analysis of different layer combinations.\n\n\n\nA1. We select the 3rd, $n//2$-th, and $(n{-}3)$-th layers, where $n$ is the total number of layers. Let the corresponding feature vectors be denoted as $l$, $m$, and $h$, respectively. The fused feature is computed as $W \\cdot \\text{concat}(l, m, h)$, where $\\text{concat}(\\cdot)$ denotes vector concatenation, and $W \\in \\mathbb{R}^{h \\times 3h}$ is a learnable projection matrix, with $h$ being the hidden dimension of the target model. The following is an ablation study on layer selection. As long as the final layer is not used and the features are fused, the performance improves significantly, while the specific choice of layers has limited impact. Furthermore, increasing the number of layers beyond three does not bring additional gains. Therefore, we opt for the $3$-rd, $n//2$-th, and $(n{-}3)$-th layers in our design.\n\n| Layers | Accept Length    | Speedup    |\n|------------|------|------|\n| $n$      | 5.37 | 3.82x | \n| $n-1$    | 5.65 | 4.00x | \n| $n-2$    | 5.76 | 4.06× | \n| $n-3$    | 5.76 | 4.05x | \n| $n-3$, $n−3$    | 5.93 | 4.26x | \n| $n-3$, $n//2$    | 6.02 | 4.31x | \n| $3$, $n//2$, $n−3$   | 6.13 | 4.40× | \n| $1$, $n//2$, $n−1$   | 6.11 | 4.38× |\n| $5$, $n//2$, $n−5$   | 6.09 | 4.37× |\n| $3$, $n//2-5$, $n//2+5$, $n−3$   | 6.11 | 4.37× |\n| $3$, $10$, $n//2$, $n-10$, $n−3$   | 6.15 | 4.39× |\n\n> Q2. Experiments are limited to LLaMA, Vicuna, and similar models (up to 70B). There is no evaluation on more recent or larger models (e.g., Qwen-32B or MoE), which limits claims of generality.\n\n\nA2. We provide additional results on larger MoE model architectures. The table below shows the acceleration performance of EAGLE-3 within the SGLang framework on LLaMA4 Scout (MoE, 109B) and LLaMA4 Maverick (MoE, 400B). It is important to note that results obtained under the production-grade SGLang framework should not be directly compared with those from standard Huggingface-based environments. \n\n| Model | Llama 4 Scout (MoE, 109B)   | Llama 4 Maverick (MoE, 400B)   |\n|------------|------|------|\n| Speedup      | 2.00x | 2.18x | \n\n> Q3. EAGLE-3 is trained with more data than baselines, which may confound the effect of the method with data scale. There is no controlled comparison under equal data conditions.\n\nA3. Figure 2 compares EAGLE-2 and EAGLE-3 under the same data scale. Futhermore, we have already included a comparison under the same data conditions with HASS—the most similar and strongest-performing baseline to our method—in Appendix B of the original manuscript. Similarly to EAGLE-2, HASS fails to scale up, while EAGLE-3 exhibits rapid performance improvements as more training data become available. EAGLE-3 consistently outperforms HASS across all data scales. For convenience, we paste the results here. Note that Data Scale = 1 refers to using the full amount of ShareGPT data. The evaluation is conducted on the MT-Bench dataset, with LLaMA-Instruct 3.1 8B as the target model.\n\n| Data Scale | 1    | 2    | 4 | 8|\n|------------|------|------|------|------|\n| EAGLE-2    | 3.16x  |3.36x  | 3.40x | 3.43x |\n| HASS   | 3.55x | 3.75x | 3.81x | 3.84x | \n| EAGGLE-3   | 3.71x | 3.99x | 4.24x | 4.40x |"}, {"review_id": "AUPl1Den6E", "reviewer": "Reviewer_szc3", "summary": "This paper proposed a new speculative sampling framework, named EAGLE3, which improves the performance of the draft model by increasing the training data quality and quantity, reusing the top/middle/bottom features for fusing, and removing the feature constrained. Experimental results demonstrated that EAGLE3 improves the inference speed compared to the baselines across various framework.", "questions": "See weakness above.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n1. The motivation of EAGLE3 is clear , speeding up the LLM inference is a fundamental problem in practice. \n2. The design of EAGLE3 is generally clear and innovative.\n3. The experimental results are solid. The performance of EAGLE3 are evaluated across various inference frameworks like siglang and vllm.\n\nWeakness\n1. As mentioned in the paper, “DeepSeek-v3 inspired the design of EAGLE3”, some discussions and comparisons between the design of deepseek-v3 and that of EAGLE3 could be added to the paper.\n2. The ablation study showed that a mix of low, middle, and high-level features improves the performance, is there any insights or discussions on why the fused features can improve the performance of the draft model? As in deepseek-v3, only the top features is taken as the input of the multi-token prediction decoder, it is recommended to add some discussions on this point.\n3. In Table 3-5, only 2 out of three algorithms (EAGLE1, EAGLE2, and EAGLE3) are evaluated, it is recommended to evaluate all of them.", "was_revised": true, "final_justification": "The authors' rebuttal has addressed my previous concerns. I will maintain my positive score.", "rebuttal": "Thank you for the thorough evaluation and insightful suggestions, which have been instrumental in refining our work.\n\n> Q1. As mentioned in the paper, “DeepSeek-v3 inspired the design of EAGLE3”, some discussions and comparisons between the design of deepseek-v3 and that of EAGLE3 could be added to the paper.\n\n\nA1. The Multi-Token Prediction (MTP) architecture in DeepSeek-V3 shares similarities with EAGLE. The key difference lies in that EAGLE reuses a single network in a loop to predict all future tokens, which requires a feature loss to align the input and output features. In contrast, DeepSeek-V3's MTP uses multiple separate networks to predict tokens at different positions, thereby avoiding the need for feature loss at the cost of increased computational overhead.\n\nThis design inspired us to remove the feature loss in EAGLE-3 (similar to DeepSeek-V3), while still using a single network (as in EAGLE), thanks to the simulation mechanism during training.\n\n> Q2. The ablation study showed that a mix of low, middle, and high-level features improves the performance, is there any insights or discussions on why the fused features can improve the performance of the draft model? As in deepseek-v3, only the top features is taken as the input of the multi-token prediction decoder, it is recommended to add some discussions on this point.\n\n\nA2. We discussed this point in the original manuscript, Lines 83–88. The original text is as follows:\n\nEAGLE and speculative sampling methods such as Medusa reuse the top-layer features of the target model, specifically the features immediately before the LM head. For an LM head with a full-rank weight matrix, the top-layer features corresponding to the logits of the next token are unique, ensuring that the information contained in these features aligns directly with the logits of the next token. However, predicting the next-next token based solely on top-layer features—which are inherently limited to the next token—poses a significant challenge. \n\n> Q3. In Table 3-5, only 2 out of three algorithms (EAGLE1, EAGLE2, and EAGLE3) are evaluated, it is recommended to evaluate all of them.\n\nA3. The difference between EAGLE-1 and EAGLE-2 lies in the draft tree generation process. However, the high batch size settings used in Tables 3 and 5 are not well-suited for tree-based drafting. This is because tree-based drafting is more computationally intensive, and the redundant GPU resources required for such speculative strategies are scarce under high batch size configurations. As a result, both methods employ chain-based drafts in these experiments, rendering EAGLE-1 and EAGLE-2 functionally equivalent under this setting. Regarding other methods, since SGLang only supports the EAGLE series, we did not include additional baselines to ensure a fair comparison.\n\nWe provide additional experimental results for Table 4 below.\n\n| Method | SGLang    | SGLang+EAGLE-1    | SGLang+EAGLE-2 | SGLang+EAGLE-3|\n|------------|------|------|------|------|\n| Throughput    | 158.34tokens/s  |219.18tokens/s  | 244.10tokens/s | 373.25tokens/s |"}]

Author rebuttal:

Meta-review: The paper presents a practically meaningful improvement to speculative decoding with consistent empirical gains, viable integration in production frameworks, and informative ablations and data scaling analyses. The main reservations—limited theoretical analysis, narrower architectural breadth, smaller MoE and production-layer speedups, and initial reproducibility gaps—reduce confidence for a spotlight/oral.
Decision: Accept (poster)