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REVIEW 4 major objections 5 minor 56 references

AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read AdaMTP claims that aligning multi-token prediction depth with token-level predictive entropy prevents noisy cross-boundary gradients and beats standard MTP in quality and speed.

desk verdict A promising adaptive MTP idea with consistent empirical gains, but the masking formula in Eq. 5 contradicts the paper's own boundary-suppression story. read the letter →

arxiv 2608.00434 v1 pith:4D4Q35O2 submitted 2026-08-01 cs.CL cs.AI

Ziqiang Cui , Han Shi , Bowei He , Yu Pan , Peiyang Liu , Shengyin Sun , Yankai Chen , Haoli Bai
show 3 more authors
Yichun Yin Xue Liu Chen Ma
This is my paper · ORCID
classification cs.CLcs.AI
keywords multi-tokenpredictionadaptivedepthentropy-basedsegmentationsemanticboundariesself-speculativedecodingrepresentationinterferenceLLMfine-tuning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that multi-token prediction (MTP) — training an LLM to forecast several future tokens at once through auxiliary heads — is undermined by a fixed prediction horizon. Natural language and code are not uniformly predictable: inside a coherent phrase or code block, uncertainty falls steadily, while it spikes at semantic boundaries. Forcing every head to predict across those spikes, the paper claims, sends noisy gradients back through the shared backbone and degrades the base model. AdaMTP detects boundaries from the base model's token-level predictive entropy, assigns each token an adaptive prediction depth, and masks any MTP loss that would cross a boundary. On Llama-3.1-8B, Qwen-2.5-7B, and Gemma-3-12B across math, code, and general benchmarks, it reports higher average scores than both next-token prediction and standard MTP, while decoding faster than standard MTP.

What carries the argument

The load-bearing mechanism is the entropy-based segmentation coupled with a dynamically masked MTP loss. Predictive entropy $E_t$ is computed from the unmodified base model so the uncertainty signal matches the model being trained; a calibrated threshold $\tau$ turns entropy surges into group boundaries, with average group size tied to the number of heads $n$. Adaptive depth $d_t$ is then defined per token: tokens inside a group predict only up to the group's end, while the boundary token predicts the whole next group. The indicator mask in the MTP objective drops every term with offset $j+1 > d_t$ from the LoRA fine-tuning loss, so the model is never asked to jump a detected semantic boundary.

What would settle it

Compute per-token entropy deltas $\Delta E_t$ on the training corpus and check whether they are systematically larger at clause and sentence boundaries than at mid-chunk positions; if they are not, the segmentation is arbitrary. A more direct check is to retrain AdaMTP with the same masking rate but boundaries placed at random positions: if random masking matches entropy-based masking on GSM8K and HumanEval, the entropy-surge hypothesis is not what carries the gain.

Watch

Extended reading notes

Core claim

At the center is a diagnosis and a fix. The diagnosis: standard MTP's auxiliary heads share the backbone's hidden representations, so a fixed horizon forces each head to predict across high-entropy transitions; the resulting cross-boundary gradients interfere with the main language-modeling head, which is why standard MTP underperforms NTP on average in the paper's results. The fix is an entropy-based segmentation: using the frozen base model, compute $E_t = -\sum_{v \in \mathcal{V}} P(v|x_{<t}) \log P(v|x_{<t})$ and split the sequence where $\Delta E_t > \tau$; then give token $x_t$ an adaptive depth $d_t$ equal to the distance to the end of its group (or the next group's length, for the boundary token), and train with the masked objective $L_{\mathrm{MTP}} = \sum_{j=1}^{n-1}\sum_t \mathbb{I}(j+1 \le d_t)\,\mathrm{L_{CE}}(\mathrm{Head}_j(h_t), x_{t+j+1})$. The paper claims this suppresses noisy supervision, preserves core abilities, and makes the MTP heads' drafts more acceptable to the verifier, which is why AdaMTP reports higher task scores and higher self-speculative speedups than standard MTP.

Load-bearing premise

The load-bearing premise is that the base model's next-token entropy has a reliable signature—it falls roughly monotonically inside a coherent chunk and jumps at chunk boundaries—and that this signature still holds while the model is being fine-tuned with LoRA.

Editorial extensions

If this is right

  • If the central claim is correct, the fixed horizon itself—not the multi-token objective—is the source of MTP's degradation: the paper reports standard MTP average scores below NTP on all three backbones, while AdaMTP sits above NTP.
  • Deeper MTP becomes usable: the head-count study on Llama-3.1-8B with GSM8K shows standard MTP accuracy falling from 11.60 at $n=2$ to 9.68 at $n=6$, while AdaMTP stays above the NTP reference and peaks at $n=4$.
  • The speedup over standard MTP is a training effect rather than a decoding change: both use the same fixed-horizon self-speculative procedure, so AdaMTP's faster decoding must come from drafts that the verifier accepts more often.
  • Adaptive-horizon decoding should cut verification cost under batched serving: the paper reports fewer candidate tokens verified per step with statistically indistinguishable accuracy, with the benefit growing as inference becomes compute-bound.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • We infer that the entropy-surge principle is not specific to MTP: any auxiliary objective that shares backbone representations should avoid supervision across entropy surges, a hypothesis testable by applying the same mask to other multi-future-token losses.
  • We infer that the entropy signal could serve as a general segmentation tool outside training, for example finding discourse boundaries or guiding retrieval or cache decisions; the paper does not explore these uses.
  • We infer that computing depths once with the frozen base model keeps AdaMTP cheap, but online depth updates during fine-tuning might capture distribution shift; the paper does not test that variant.
  • We infer that a natural next test is applying the same adaptive masking during full pretraining rather than SFT retrofit, to see whether the representation-interference benefit scales beyond the fine-tuning regime.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes AdaMTP, an adaptive multi-token prediction training scheme for large language models. It uses token-level predictive entropy from the frozen base model to segment training sequences into variable-length groups, assigns each token an adaptive prediction depth (Eq. 5), and applies a dynamically masked MTP loss (Eq. 7) that is intended to suppress cross-boundary predictions. The authors evaluate on three backbones (Llama-3.1-8B, Qwen-2.5-7B, Gemma-3-12B) across eight benchmarks, comparing AdaMTP with standard NTP and fixed-horizon MTP in both task accuracy and inference speedup. A secondary adaptive-horizon decoding mode is also discussed.

Significance. If the central claim held, AdaMTP would be a low-cost training-time modification with consistent quality and speed gains over fixed-horizon MTP, and the paper's scope is appropriate: three backbones, eight benchmarks, and both performance and efficiency measures. The problem is well motivated, and the self-speculative decoding framing makes the efficiency comparison meaningful. However, the current evidence is not yet convincing: the reported effect sizes are small and unreplicated, and one of the core equations contradicts the paper's stated masking mechanism. The paper does not provide code, proofs, or a parameter-free derivation; its contribution is empirical, so the reliability of the experiments is load-bearing.

major comments (4)
  1. [Section 3.1, Eq. (5); Section 3.2, Eq. (7)] The implemented mask does not suppress cross-boundary predictions for boundary tokens. For t = e_k - 1, the token immediately preceding a detected split, Eq. (5) sets d_t = |G_{k+1}|, so Eq. (7) retains every auxiliary loss with j+1 <= |G_{k+1}|; all of these targets lie in the next group, across the detected boundary. Since tau is calibrated so that the average group size equals n, a nontrivial fraction of tokens (about 1/n, i.e., 25% at n=4) receives no masking on its auxiliary losses at all. This contradicts the Abstract's and Section 3.2's claim that AdaMTP 'suppresses the loss for predictions that cross these boundaries.' If the experiments used a different boundary-token rule, it must be stated; as written, the reported gains cannot be attributed to suppressing cross-boundary gradients.
  2. [Section 4.2, Table 1] The performance differences supporting 'consistently outperforms' are small and are reported without error bars, multiple seeds, or significance tests. For example, on Llama-3.1-8B the HumanEval+ scores are identical for MTP and AdaMTP (35.98), and on Gemma-3-12B the MBPP+ scores are identical (54.50); several other cells differ by less than one point. The average gains over MTP are 0.95, 1.59, and 1.13 points on the three backbones. Without estimates of variance or paired tests across seeds, these results are compatible with training noise, so the claim of consistent superiority is not yet established.
  3. [Section 3.1; Section 4.4] The entropy-based segmentation is never ablated against a non-entropy control. The threshold tau is calibrated so that the average group size equals n, and Figure 1 illustrates the entropy pattern on a single example, but the paper does not compare against random segmentation or fixed blocks of size n under otherwise identical training. Such an ablation is necessary to attribute the gains to the entropy signal rather than to any variable-depth masking. The calibration procedure for tau, including the search range and the resulting values, is also not reported.
  4. [Section 4.5] The adaptive-horizon decoding mode is claimed to yield 'clear throughput gains as batch size increases,' but no end-to-end throughput or latency measurement under large-batch, compute-bound serving is reported; only the average number of verified tokens per step is shown. Moreover, the statement that the adaptive strategy keeps accuracy 'statistically indistinguishable' from the fixed-horizon baseline appears without any reported statistical test. Please either provide the missing measurements or soften the claims to match the evidence.
minor comments (5)
  1. [Eq. (4)] The indexing of E_t is ambiguous: the text says 'entropy of the next-token distribution at each position t,' but the notation E_t = H(P(·|x_<t)) is the entropy for the token at position t. Please align the notation with the segmentation indices in Eq. (5).
  2. [Section 3.1] The calibration of tau is described only as a dataset-level search; please report the search space, the criterion, and the resulting tau values for each backbone and dataset.
  3. [Figure 1] The claimed 'approximately monotonically decreasing' entropy pattern is illustrated on one sentence; please quantify this pattern on the training corpus or soften the generalization.
  4. [Table 2] Speedup ratios are reported as single numbers without measurement variability or a description of the evaluation protocol (number of prompts, generation length, batch size, hardware). Please add these details so the ratios are reproducible.
  5. [Abstract; Section 4.2] The phrase 'consistently outperforms' should be qualified where individual benchmark cells are ties, such as HumanEval+ on Llama-3.1-8B and MBPP+ on Gemma-3-12B in Table 1.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: AdaMTP's entropy segmentation and masked objective are self-contained; the flagged boundary-token issue is a consistency concern, not a circular reduction.

full rationale

I walked the claimed derivation chain: (i) token-level predictive entropy E_t is computed from the frozen base model (Eq. 4); (ii) segmentation is done by thresholding Delta-E_t with tau, and tau is calibrated only to make the average group size roughly equal to the fixed head count n; (iii) per-token adaptive depths d_t are defined in Eq. 5; (iv) the masked MTP loss in Eq. 7 is a standard cross-entropy with an indicator; and (v) the reported gains are empirical comparisons on external benchmarks. None of these steps fits a parameter to the benchmark outcomes it is used to explain, and no quantity advertised as a prediction is actually a fitted value renamed as a result. The threshold tau is a hyperparameter tied to the maximum prediction depth n, and lambda is a manually chosen loss weight; neither smuggles the central claim in by construction. The citations to Medusa and L-MTP support standard warm-up practice and training-corpus alignment rather than importing the core novelty, and I found no load-bearing self-citation chain or imported uniqueness theorem. The skeptical objection that Eq. 5 gives boundary tokens the full length of the next group, so Eq. 7 retains the offset-1 prediction crossing the detected boundary, is a legitimate internal-consistency and attribution-of-effect question about whether the mask matches the paper's stated motivation; however, that is a correctness risk rather than a circularity in which a derived result is equivalent to its input by definition. Therefore I find no circular step.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the entropy-boundary assumption, the interference hypothesis, and the lossless verification assumption. The fitted threshold tau and hyperparameters n and lambda are chosen by the authors, but none of these make the result circular; they are conventional hyperparameters.

free parameters (4)
  • tau (entropy surge threshold) = calibrated via dataset-level search so average group size matches n
    Controls where the sequence is split into groups; directly determines adaptive depths d_t (Eq. 5).
  • n (prediction horizon) = 4 (default)
    Number of heads including auxiliary heads; central to the MTP objective; set by the authors.
  • lambda (MTP loss weight) = 0.1
    Weights the masked MTP loss in the total loss (Eq. 8); chosen without reported sensitivity analysis.
  • LoRA rank and alpha = r=32, alpha=16
    Hyperparameters for the joint fine-tuning stage; not central to the method but affect results.
assumptions (4)
  • domain assumption Token-level predictive entropy decreases approximately monotonically within a semantic group and surges at semantic boundaries (Figure 1).
    Core premise of the segmentation algorithm; stated in Section 3.1 and visualized in Figure 1, but not quantitatively validated on the training corpus.
  • domain assumption Auxiliary heads sharing the backbone with the main head cause representation interference when forced to predict across high-entropy boundaries.
    The paper's hypothesis for why standard MTP underperforms NTP; used to justify the masking design. Presented as a conclusion from experiments rather than independently tested.
  • domain assumption The frozen base model's next-token entropy is an accurate proxy for the target model's intrinsic predictability during fine-tuning.
    Segmentation is computed from the unmodified pretrained model, but training uses LoRA updates; the paper assumes the entropy landscape does not shift materially.
  • domain assumption Self-speculative decoding with tree verification is lossless and preserves the target distribution.
    The inference speedup claims rely on the standard Medusa-style verification guarantee (Cai et al., 2024), not re-derived here.

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Cite this review

Pith. "Pith review of AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction." pith.science (2026). https://pith.science/paper/4D4Q35O2

@misc{pith2026260800434,
  author       = {Pith},
  title        = {Pith review of: AdaMTP: An Adaptive Training Paradigm for Multi-Token Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4D4Q35O2}},
  note         = {Machine review of arXiv:2608.00434}
}
read the original abstract

Multi-Token Prediction (MTP) has emerged as an effective paradigm that augments a shared Large Language Model backbone with auxiliary heads, training the model to predict several future tokens in parallel to enrich its supervision signal and accelerate inference. However, existing training frameworks adopt a rigid, fixed-length prediction horizon, disregarding the highly non-uniform information density of natural language and code. Forcing the auxiliary heads to predict across high-entropy semantic boundaries injects noisy, conflicting training signals; because these heads share the backbone's latent representations, the resulting gradients backpropagate and interfere with the model's core capabilities. We propose AdaMTP, an adaptive training paradigm that dynamically aligns the prediction horizon with the intrinsic predictability of the sequence. At its core, an entropy-based segmentation algorithm leverages the base model to detect sudden surges in uncertainty as semantic boundaries, partitioning sequences into variable-length groups. Each token is assigned an adaptive prediction depth, and a dynamically masked MTP objective suppresses the loss for predictions that cross these boundaries, attenuating the noisy gradients that degrade the backbone. Across mathematical reasoning, code generation, and general benchmarks on three backbones (Llama-3.1-8B, Qwen-2.5-7B, Gemma-3-12B), AdaMTP consistently outperforms standard MTP in both task performance and inference speedup.

Figures

Figures reproduced from arXiv: 2608.00434 by the authors.

Figure 1
Figure 1. Token-level predictive entropy of the LLM [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of AdaMTP training paradigm. (Left) Entropy-Based Data Segmentation: using the base model’s next-token entropy, the sequence is split into variable-length groups at points of sudden entropy surge, and each token is assigned an adaptive prediction depth. (Right) Dynamically Masked MTP Training: the shared LLM backbone feeds n parallel heads that predict future tokens; losses for predictions falling within ea… view at source ↗
Figure 3
Figure 3. Impact of the number of prediction heads [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Average number of candidate tokens verified [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Works this paper leans on

56 extracted references · 4 canonical work pages

  1. [1]

    arXiv preprint arXiv:2505.17505 , year=

    L-mtp: Leap multi-token prediction beyond adjacent context for large language models , author=. arXiv preprint arXiv:2505.17505 , year=

    arXiv

  2. [2]

    arXiv preprint arXiv:2505.10518 , year=

    Multi-token prediction needs registers , author=. arXiv preprint arXiv:2505.10518 , year=

    arXiv

  3. [3]

    arXiv preprint arXiv:2410.17765 , year=

    Faster language models with better multi-token prediction using tensor decomposition , author=. arXiv preprint arXiv:2410.17765 , year=

    arXiv

  4. [4]

    arXiv preprint arXiv:2510.14751 , year=

    Beyond multi-token prediction: Pretraining llms with future summaries , author=. arXiv preprint arXiv:2510.14751 , year=

    arXiv

  5. [5]

    arXiv preprint arXiv:2404.19737 , year=

    Better & faster large language models via multi-token prediction , author=. arXiv preprint arXiv:2404.19737 , year=

    arXiv

  6. [6]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Byte latent transformer: Patches scale better than tokens , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  7. [7]

    arXiv preprint arXiv:2412.19437 , year=

    Deepseek-v3 technical report , author=. arXiv preprint arXiv:2412.19437 , year=

    arXiv

  8. [8]

    arXiv preprint arXiv:2512.24617 , year=

    Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space , author=. arXiv preprint arXiv:2512.24617 , year=

    arXiv

Show all 56 references
  1. [9]

    arXiv preprint arXiv:2503.21801 , year=

    Efficient joint prediction of multiple future tokens , author=. arXiv preprint arXiv:2503.21801 , year=

    arXiv

  2. [10]

    arXiv preprint arXiv:2509.18362 , year=

    Fastmtp: Accelerating llm inference with enhanced multi-token prediction , author=. arXiv preprint arXiv:2509.18362 , year=

  3. [11]

    arXiv preprint arXiv:2401.10774 , year=

    Medusa: Simple llm inference acceleration framework with multiple decoding heads , author=. arXiv preprint arXiv:2401.10774 , year=

    arXiv

  4. [12]

    arXiv preprint arXiv:2508.19228 , year=

    Predicting the order of upcoming tokens improves language modeling , author=. arXiv preprint arXiv:2508.19228 , year=

  5. [13]

    arXiv preprint arXiv:2507.11851 , year=

    Your llm knows the future: Uncovering its multi-token prediction potential , author=. arXiv preprint arXiv:2507.11851 , year=

    arXiv

  6. [14]

    arXiv preprint arXiv:2505.07608 , year=

    MiMo: Unlocking the Reasoning Potential of Language Model--From Pretraining to Posttraining , author=. arXiv preprint arXiv:2505.07608 , year=

    arXiv

  7. [15]

    Findings of the Association for Computational Linguistics: EMNLP 2020 , pages=

    ProphetNet: Predicting future n-gram for sequence-to-SequencePre-training , author=. Findings of the Association for Computational Linguistics: EMNLP 2020 , pages=

    2020

  8. [16]

    journal of machine learning research , volume=

    Quantized neural networks: Training neural networks with low precision weights and activations , author=. journal of machine learning research , volume=

  9. [17]

    arXiv preprint arXiv:2306.07629 , year=

    Squeezellm: Dense-and-sparse quantization , author=. arXiv preprint arXiv:2306.07629 , year=

    arXiv

  10. [18]

    Proceedings of machine learning and systems , volume=

    Awq: Activation-aware weight quantization for on-device llm compression and acceleration , author=. Proceedings of machine learning and systems , volume=

  11. [19]

    Advances in neural information processing systems , volume=

    Llm-pruner: On the structural pruning of large language models , author=. Advances in neural information processing systems , volume=

  12. [20]

    Advances in Neural Information Processing Systems , volume=

    Disp-llm: Dimension-independent structural pruning for large language models , author=. Advances in Neural Information Processing Systems , volume=

  13. [21]

    International conference on machine learning , pages=

    Sparsegpt: Massive language models can be accurately pruned in one-shot , author=. International conference on machine learning , pages=. 2023 , organization=

    2023

  14. [22]

    arXiv preprint arXiv:2306.11695 , year=

    A simple and effective pruning approach for large language models , author=. arXiv preprint arXiv:2306.11695 , year=

    arXiv

  15. [23]

    arXiv preprint arXiv:2306.08543 , year=

    Minillm: Knowledge distillation of large language models , author=. arXiv preprint arXiv:2306.08543 , year=

    arXiv

  16. [24]

    arXiv preprint arXiv:1503.02531 , year=

    Distilling the knowledge in a neural network , author=. arXiv preprint arXiv:1503.02531 , year=

    arXiv

  17. [25]

    arXiv preprint arXiv:2304.03277 , year=

    Instruction tuning with gpt-4 , author=. arXiv preprint arXiv:2304.03277 , year=

    arXiv

  18. [26]

    Findings of the Association for Computational Linguistics: ACL 2023 , pages=

    Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes , author=. Findings of the Association for Computational Linguistics: ACL 2023 , pages=

    2023

  19. [27]

    Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers) , pages=

    Large language models are reasoning teachers , author=. Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers) , pages=

  20. [28]

    International conference on machine learning , pages=

    Transformers are rnns: Fast autoregressive transformers with linear attention , author=. International conference on machine learning , pages=. 2020 , organization=

    2020

  21. [29]

    Advances in neural information processing systems , volume=

    Parallelizing linear transformers with the delta rule over sequence length , author=. Advances in neural information processing systems , volume=

  22. [30]

    arXiv preprint arXiv:1904.10509 , year=

    Generating long sequences with sparse transformers , author=. arXiv preprint arXiv:1904.10509 , year=

    1904 arXiv

  23. [31]

    arXiv preprint arXiv:2502.13189 , year=

    Moba: Mixture of block attention for long-context llms , author=. arXiv preprint arXiv:2502.13189 , year=

    arXiv

  24. [32]

    arXiv preprint arXiv:2405.04434 , year=

    Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model , author=. arXiv preprint arXiv:2405.04434 , year=

    arXiv

  25. [33]

    Advances in neural information processing systems , volume=

    Flashattention: Fast and memory-efficient exact attention with io-awareness , author=. Advances in neural information processing systems , volume=

  26. [34]

    arXiv preprint arXiv:2307.08691 , year=

    Flashattention-2: Faster attention with better parallelism and work partitioning , author=. arXiv preprint arXiv:2307.08691 , year=

    arXiv

  27. [35]

    Proceedings of the 29th symposium on operating systems principles , pages=

    Efficient memory management for large language model serving with pagedattention , author=. Proceedings of the 29th symposium on operating systems principles , pages=

  28. [36]

    Advances in neural information processing systems , volume=

    Sglang: Efficient execution of structured language model programs , author=. Advances in neural information processing systems , volume=

  29. [37]

    International Conference on Machine Learning , pages=

    Fast inference from transformers via speculative decoding , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  30. [38]

    arXiv preprint arXiv:2302.01318 , year=

    Accelerating large language model decoding with speculative sampling , author=. arXiv preprint arXiv:2302.01318 , year=

    arXiv

  31. [39]

    CCF International Conference on Natural Language Processing and Chinese Computing , pages=

    Multi-candidate speculative decoding , author=. CCF International Conference on Natural Language Processing and Chinese Computing , pages=. 2025 , organization=

    2025

  32. [40]

    arXiv preprint arXiv:2310.08461 , year=

    Distillspec: Improving speculative decoding via knowledge distillation , author=. arXiv preprint arXiv:2310.08461 , year=

    arXiv

  33. [41]

    Advances in Neural Information Processing Systems , volume=

    Blockwise parallel decoding for deep autoregressive models , author=. Advances in Neural Information Processing Systems , volume=

  34. [42]

    arXiv preprint arXiv:2401.15077 , year=

    Eagle: Speculative sampling requires rethinking feature uncertainty , author=. arXiv preprint arXiv:2401.15077 , year=

    arXiv

  35. [43]

    Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 , pages=

    Specinfer: Accelerating large language model serving with tree-based speculative inference and verification , author=. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 , pages=

  36. [44]

    Advances in Neural Information Processing Systems , volume=

    Spectr: Fast speculative decoding via optimal transport , author=. Advances in Neural Information Processing Systems , volume=

  37. [45]

    arXiv preprint arXiv:2412.08821 , year=

    Large concept models: Language modeling in a sentence representation space , author=. arXiv preprint arXiv:2412.08821 , year=

    arXiv

  38. [46]

    arXiv preprint arXiv:2103.03874 , year=

    Measuring mathematical problem solving with the math dataset , author=. arXiv preprint arXiv:2103.03874 , year=

    arXiv

  39. [47]

    arXiv preprint arXiv:2306.08568 , year=

    Wizardcoder: Empowering code large language models with evol-instruct , author=. arXiv preprint arXiv:2306.08568 , year=

    arXiv

  40. [48]

    Code alpaca: An instruction-following llama model for code generation , author=

  41. [49]

    The twelfth international conference on learning representations , year=

    Let's verify step by step , author=. The twelfth international conference on learning representations , year=

  42. [50]

    arXiv preprint arXiv:2110.14168 , year=

    Training verifiers to solve math word problems , author=. arXiv preprint arXiv:2110.14168 , year=

    arXiv

  43. [51]

    arXiv preprint arXiv:2108.07732 , year=

    Program synthesis with large language models , author=. arXiv preprint arXiv:2108.07732 , year=

    arXiv

  44. [52]

    Advances in neural information processing systems , volume=

    Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation , author=. Advances in neural information processing systems , volume=

  45. [53]

    arXiv preprint arXiv:2107.03374 , year=

    Evaluating large language models trained on code , author=. arXiv preprint arXiv:2107.03374 , year=

    arXiv

  46. [54]

    arXiv preprint arXiv:2009.03300 , year=

    Measuring massive multitask language understanding , author=. arXiv preprint arXiv:2009.03300 , year=

    2009 arXiv

  47. [55]

    arXiv preprint arXiv:2311.07911 , year=

    Instruction-following evaluation for large language models , author=. arXiv preprint arXiv:2311.07911 , year=

    arXiv

  48. [56]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. Iclr , volume=

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.