Title: EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Venue: NeurIPS 2025
Accepted: yes
Keywords: Speculative decoding
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

Abstract

The sequential nature of modern LLMs makes them expensive and slow, and speculative 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 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. The code is available at https://github.com/SafeAILab/EAGLE .

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1 Introduction

Modern Large Language Models (LLMs) are being applied to more domains, with their improved capabilities driven by scaling model parameters-some LLMs now exceed hundreds of billions of

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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 o1 and DeepSeekR1 [1] 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 of 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 allows multiple tokens to be produced in a single forward pass, significantly reducing inference latency. As a state-of-the-art speculative sampling method, EAGLE [2] decodes an LLM by leveraging 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 3, EAGLE trains the draft model by feeding all previous features f 1 , ..., f t into the target model to predict the next feature ˆ f t +1 , with the training data feature f t +1 as the label. In the test phase (the second box), EAGLE autoregressively predicts the next feature ˆ f and then uses the target model's LM head to obtain the draft token ˆ t . By leveraging the rich information from the target model, EAGLE achieves significantly better acceleration compared to vanilla speculative sampling. Subsequent methods such as HASS [3] and Falcon [4] also adopt the approach of predicting the next feature using the current feature sequence.

Recent LLMs have increasingly relied on larger training datasets to achieve better performance. For example, LLaMA series models with sizes of 7B (8B) have used 1T, 2T, and 15T tokens of training data for LLaMA 1 [5], LLaMA 2 [6], and LLaMA 3 [7], respectively, resulting in significant improvements across various metrics while keeping the model architecture and inference cost largely unchanged. Similarly, we aim to improve the acceptance rate and acceleration ratio of EAGLE by increasing its training data.

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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

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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 loss function consists of two components: the feature prediction loss l fea and the token prediction loss l token . 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 4, the acceptance rate 0 -α of the first draft token improves significantly. However, the output of the draft model in the training phase, denoted as ˆ a t +1 , is far away from the ground-truth f t +1 , causing the input sequence f 1 , f 2 , · · · , f t , ˆ a t +1 in the test phase to deviate significantly from the training distribution, resulting in a very low acceptance rate 1 -α for the second draft token, as shown in Figure 4. We can address this issue by moving the output ˆ a t +1 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 as training-time test.

EAGLE and speculative sampling methods such as Medusa [8] 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. Fortunately, the training-time test technique described above enables the use of features from intermediate layers instead of relying solely on the top layer, as the feature prediction loss l fea 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: We remove the feature prediction 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.

A new scaling law for inference acceleration in LLMs: With the new architecture, we observe that increasing the amount of training data for the draft model leads to a proportional increase in the speedup ratio of EAGLE-3. This scaling behavior was not observed in the original EAGLE architecture, as shown in Figure 2

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 [9], a production-grade 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.

2 Preliminaries

2.1 Speculative Sampling

Speculative sampling [10, 11, 12, 13] is a lossless LLM acceleration technique that alternates between drafting and verification, where drafting is performed at low cost and verification is parallelized, corresponding to the generation of drafts and the verification process, respectively. We use t i to denote the i -th token and T a : b to represent the token sequence t a , t a +1 , · · · , t b . When T 1: j 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. ˆ T j +1: j + k , while also recording the probability ˆ p for each token.

In the verification stage, speculative sampling invokes the target model to evaluate the draft ˆ T j +1: j + k and records its probability p . Speculative sampling then determines the acceptance of draft tokens sequentially, from front to back. For token ˆ t j + i , the probability of acceptance is given by min(1 , p j + i ( ˆ t j + i ) / ˆ p j + i ( ˆ t j + i )) . If the token is accepted, the process moves to the next token. Otherwise, a token is sampled from the distribution norm (max(0 , p j + i -ˆ p j + i )) to replace ˆ t j + i , and the remaining tokens in the draft are discarded. Appendix A.1 of [10] proves that speculative sampling is consistent with the distribution of vanilla autoregressive decoding.

2.2 EAGLE and EAGLE-2

The draft model with limited capacity struggles to precisely approximate the large-scale target model. EAGLE leverages the top-layer features of the target model as additional information and performs autoregression at 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 a 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 DeepSeek-v3 [14], which in turn inspired new architectural designs in EAGLE-3.

EAGLE [2] and Medusa [8], among others, use tree-shaped drafts, where the structure of the draft tree is predefined, static, and context-independent. The difficulty of drafting is 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-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 an example, as 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, 'I'. We record the low, middle, and high-level feature sequences from the target model's forward pass, denoted as l , m , and h , respectively. We concatenate the k -dimensional vectors l , m , and h to form a 3 k -dimensional vector, then pass it through a fully connected (FC) layer to reduce it to k -dimensions, obtaining a feature g that integrates information from different layers. Here, k refers to the hidden size of the target model.

Our goal is to generate a draft token sequence with the prefix 'How can I'. By inputting only g how and g can, the draft model cannot access the random sampling process. Therefore, similar to EAGLE [2],

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How can I are we you How can I ✓ ✓ ✓ ✓ ✓ ✓ ✓ you help are we do you help it we introduce the embedding e I of the sampled token 'I'. 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, we input a I into the LM head and sample to obtain the draft token 'do'. ...

How can I can I do I do it How can ✓ I do it do help it ✓ ✓ ✓ it In Step 1, with the prefix 'How can', we reuse g how and g can from the target model. In Step 2, the prefix becomes 'How can I'. Ideally, we would reuse g how, g can, and g I from the target model. However, this is not possible because the token 'I' has not yet been checked by the target model, and we cannot obtain g I. Instead, we use the output a I from the draft model in the previous step to replace g I, and concatenate a I with the embedding e do of the sampled result 'do' as the input to the draft model in Step 1. In Step 3, we similarly cannot obtain g do, so we use a do as a replacement, concatenating a do with e it as the input to the draft model. The same approach is followed for subsequent steps.

3.2 Draft Model Training

The input to the draft model in EAGLE is either, or at least approximately, the top-layer features f 1 , f 2 , · · · , f t of the target model. In contrast, the input to the draft model in EAGLE3 may include the features g 1 , g 2 , · · · , g t from the target model, or it may include the output a t +1 , a t +2 · · · , a t + j from the draft model. Therefore, we need to train the draft model to adapt to different inputs. During training, we perform test steps, where we generate a and feed it back into the draft model for further training.

The core of the draft model in EAGLE-3 is a 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 6, the original training data is a sequence of length 3, 'How can I', with a normal sequential dependency in the context. Therefore, the attention mask is a 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 'I'. 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 6. 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 is to mitigate the error accumulation caused by inaccurate feature predictions in EAGLE. HASS still performs feature prediction, includes a feature prediction loss l fea, 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 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 a new scaling law for inference acceleration which was never found before. Figure 1 and Table 1 also shows the speedup of EAGLE-3 and HASS, with EAGLE-3 demonstrating significantly better performance.

4 Experiments

Models. We conduct experiments with open-source chat and reasoning models, including Vicuna 13B [16], LLaMA-Instruct 3.1 8B, LLaMA-Instruct 3.3 70B [7], and DeepSeek-R1-Distill-LLaMA 8B [17]. Due to the GPU constraint, we are unable to test EAGLE-3 on models larger than 70B.

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], GSM8K [21], 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 τ : The average number of tokens generated per draftingverification cycle, which corresponds to the number of tokens accepted from the draft.

Acceptance Rate n -α : The proportion of draft tokens accepted, which directly reflects the draft model's approximation to the target model. Following EAGLE's setup, we use a 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 -α to represent the acceptance rate when the input contains n 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 f 1 , f 2 , · · · , f i , ˆ f i +1 , · · · , ˆ f i + n , where f is the exact value and ˆ f is the draft model's estimate. Similarly, we use n -α to represent the acceptance rate in EAGLE-3 when the input contains n self-predicted values a , i.e., the acceptance rate for inputs g 1 , g 2 , · · · , g i , a i +1 , · · · , a i + n , where g is the fused feature from the target model.

Implementation. We use the AdamW optimizer, with beta values ( β 1 , β 2 ) set to (0.9, 0.95) and implemented gradient clipping of 0.5. The learning rate is set to 5e-5. We simulate 5 steps during training-time test. We use ShareGPT and UltraChat-200K [24] as training data, containing approximately 68K and 464K data entries, respectively. We call the target model to generate responses rather than using a fixed dataset. For the reasoning model DeepSeek-R1-Distill-LLaMA 8B, we also use the OpenThoughts-114k-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 all methods accelerating the same target model is identical.

Comparison. We use vanilla autoregressive decoding as the baseline, which serves as the benchmark for speedup ratios (1.00x). We compare EAGLE-3 with recent lossless speculative sampling methods, including standard speculative sampling [10, 11, 25], PLD [26], Medusa [8], Lookahead [27], Hydra [28], HASS [3], EAGLE [2], and EAGLE-2 [15].

4.1 Effectiveness

Figure 1 and Table 1 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-6.5x compared to vanilla autoregressive generation, with a 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 HumanEval, achieving a speedup ratio of up to 6.5x and an average acceptance length of up to 7.5. DeepSeek-R1-Distill-LLaMA 8B is an exception, with the highest

speedup ratio on the mathematical reasoning dataset GSM8K. This may be because we trained the draft model of DeepSeek-R1-Distill-LLaMA 8B using the OpenThoughts-114k-math dataset.

Figure 7 shows the acceptance rates of EAGLE and EAGLE-3 on MT-bench with LLaMA-Instruct 3.1 8B as the target model. The acceptance rate of EAGLE-3 is significantly higher than that of EAGLE. As the input 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.

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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 a mix of low, middle, and high-level features, and third, the increase of training data. We conducted an ablation study on MT-bench with LLaMA-Instruct 3.1 8B as 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 2 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 was never observed in the previous works.

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-grade frameworks. The performance of EAGLE-3 for large batches on a single H100 GPU and LLaMA-Instruct 3.1 8B in the SGLang v0.4.4 environment [9] was evaluated in Table 3. This part of the experiment did not use the tree structure, the chain length was set to 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.

We also tested the throughput of EAGLE-3 at batch size = 1 on H100 when the target model is LLaMA-Instruct 3.1 8B and the testing dataset is MT-bench. The results are shown in Table 4.

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], a widely used production-grade framework, and the results on RTX3090 and LLaMAInstruct 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 at 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-Bench.

5 Related Work

Many methods have been used to accelerate inference in LLMs, such as quantization [30, 31, 32, 33, 34] and distillation [35]. These methods generally have trade-offs, where there is a need to balance model performance with acceleration benefits.

Speculative sampling uses the target model for verification to ensure lossless acceleration. Early speculative decoding methods [36, 37] accelerated generation in greedy settings, while [10, 11] introduced speculative sampling to extend the draft verification framework to non-greedy generation. Many subsequent works have improved upon speculative sampling. EAGLE [2], EAGLE-2 [15], Medusa [8], and Hydra [28] reused the features of the target model. Based on the framework of EAGLE, HASS [3] simulates a multistep draft process during training to mitigate the issues of training-inference inconsistency and error accumulation in EAGLE. GLIDE and CAPE [38] reuse the target model's KV cache, while methods [39, 40, 41, 42, 43, 44, 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 l token, while EAGLE-3 removes it to improve model capacity. Third, unlike HASS, EAGLE-3 exhibits a clear scaling law trend. Finally, EAGLE-3 significantly outperforms HASS, as demonstrated in Figure 1 and Table 1.

6 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 to benefit from the augmentation of training data, achieving a maximum speedup of 6.5x.

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A Implementation Details

Vanilla: We use models from the Huggingface.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-Chat draft models use the officially released weights, while LLaMA3Instruct 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 a 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 us to increase the draft tree depth from 6 to 8 while keeping the number of nodes the same as in 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 to enforce correct dependencies, which essentially simplifies tree attention into a fixed-shape form (as illustrated in Figure 6). 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 in EAGLE and mitigating it through tree attention-based simulation. In contrast, EAGLE-3 focuses on a different issue: the inability of EAGLE to benefit from data scaling. EAGLE-3 attributes this limitation to the feature prediction constraint-an 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 to scale with data is the core contribution of EAGLE-3.

Figure 8 illustrates the performance of EAGLE-3 and HASS across different training data scales. Similar 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 are not well-suited for multi-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 on smaller datasets.

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Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strength
1. The motivation of EAGLE3 is clear , speeding up the LLM inference is a fundamental problem in practice. 
2. The design of EAGLE3 is generally clear and innovative.
3. The experimental results are solid. The performance of EAGLE3 are evaluated across various inference frameworks like siglang and vllm.

Weakness
1. 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.
2. 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.
3. In Table 3-5, only 2 out of three algorithms (EAGLE1, EAGLE2, and EAGLE3) are evaluated, it is recommended to evaluate all of them.

QUESTIONS:
See weakness above.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-16

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- **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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nstrength: \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\n\nQUESTIONS:\nnone\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}