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LLM Pretraining with Continuous Concepts

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper proposes Continuous Concept Mixing (CoCoMix), which augments next-token prediction with continuous concept prediction and shows this improves sample efficiency and downstream performance.

desk verdict Clever pretraining idea, but the token-efficiency headline ignores a ~2x compute overhead, so the paper needs a compute-normalized comparison before the main claim holds. read the letter →

arxiv 2502.08524 v1 pith:32AGNVES submitted 2025-02-12 cs.LG cs.CL

Jihoon Tack , Jack Lanchantin , Jane Yu , Andrew Cohen , Ilia Kulikov , Janice Lan , Shibo Hao , Yuandong Tian
show 2 more authors
Jason Weston Xian Li
This is my paper · ORCID
classification cs.LGcs.CL
keywords languagemodelpretrainingnext-tokenpredictionsparseautoencodersconceptknowledgedistillationweak-to-strongsupervisioninterpretabilitysampleefficiency
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 asks whether next-token prediction alone is the right training signal for language models, and answers with a pretraining method that adds a second, semantic target: predicting "continuous concepts" distilled from a pretrained teacher. The concepts are extracted by a sparse autoencoder from the middle-layer hidden state of a 124M GPT-2 model; at each position the student must predict the teacher's four most output-influential concept indices. The predicted concepts are compressed into one vector and interleaved into the student's hidden state, so the model reads both a token and a concept at every step. The central empirical claim is that this makes pretraining more sample-efficient and more accurate: a 1.38B model reaches the same OpenWebText validation perplexity as standard next-token training with 21.5% fewer tokens, and downstream accuracy improves across seven reasoning benchmarks. A sympathetic reading is that concept-level supervision works especially well in weak-to-strong settings, where a smaller teacher guides a larger student better than probability-matching distillation.

What carries the argument

The load-bearing object is the continuous concept vector $\hat{c}_t = W\,\mathrm{TopK}(z_t) + b$, where $z_t$ is the student's concept logit at position $t$, $\mathrm{TopK}$ keeps the largest entries, and $W,b$ are learned. It is produced from a linear prediction head trained with $\mathcal{L}_{\mathrm{concept}}$, a cross-entropy loss whose labels are the teacher's top-4 concept indices selected by the attribution score $a_t = c^{\mathrm{pre}}_t \odot \nabla_{c_t}(-\log f_{\mathrm{con}}(x_{t+1}\mid D(c_t), h_{<t}))$. That formula is what lets the method prefer concepts that causally influence the teacher's next-token prediction, rather than merely the largest activations. The concept vector is then interleaved as a separate unit between token hidden states, so downstream transformer layers can use it as an additional input; the paper's ablations show this interleaving, not just the prediction loss, is required for the reported gains.

What would settle it

A direct falsifier: train CoCoMix with the teacher's concept indices replaced by random indices at each position, keeping everything else identical; if the validation perplexity and downstream accuracy remain at the reported levels, then the claimed benefit does not come from semantic concepts. A cheaper version is the paper's own attribution ablation: when top-4 concepts are chosen by raw activation rather than attribution, sample efficiency drops by 17.5%, but the random-selection condition is not reported, so it would settle whether any concept signal — or just any extra interleaved vector — is doing the work.

Watch

Extended reading notes

Core claim

On the paper's own terms, CoCoMix's discovery is that the discrete token stream is not the only useful supervision channel in pretraining: a model trained to also predict sparse-autoencoder concepts — selected by a gradient-times-activation attribution score rather than by raw activation strength — learns faster and better. The mechanism is end-to-end: the student's hidden state at a chosen layer is fed to a linear head that outputs concept logits, a cross-entropy loss pushes those logits toward the teacher's top-4 concepts, and a learnable compression layer turns the top-k predicted logits into a continuous vector interleaved with the token hidden states. Ablations identify both pieces as necessary: concept prediction alone gives a modest gain, interleaving alone is weak, and the combination is what produces the reported improvements. The same recipe also outperforms knowledge distillation and pause-token training, and the predicted concept logits can be amplified during generation to steer the model's output, which the authors present as evidence that the model has internalized the teacher's concept space.

Load-bearing premise

The load-bearing assumption is that the fixed sparse-autoencoder concept space of a 124M GPT-2 teacher, together with the top-4 gradient-times-activation attribution picks, provides useful supervision for students of a different size and even for out-of-corpus data like OpenWebMath.

Editorial extensions

If this is right

  • Standard next-token prediction is not the only effective training objective: adding a continuous concept-prediction loss lowers validation perplexity at matched token budgets.
  • Weak-to-strong supervision works better with concept targets than with output-probability distillation, so a smaller teacher can guide a larger student.
  • The trained model can be steered at inference time by amplifying specific predicted concept logits, which produces outputs aligned with those concepts.
  • The gains grow with training length and survive distribution shift to a math corpus, where knowledge distillation falls behind standard training.

Reading between the lines

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

  • An extension the paper leaves to future work is learning the concept space online during pretraining instead of fixing it to a pretrained teacher's sparse autoencoder; if that works it would remove the distillation dependency, but it would need a stabilization mechanism for the concept targets.
  • The 17.5% sample-efficiency gain of attribution over raw activation suggests that selection quality matters more than concept coverage; a natural test is whether cheaper selection rules, such as random top-4 indices or frequency-based concepts, retain part of the gain.
  • Because the method interleaves extra vectors into the hidden state, it is close in spirit to pause-token training; a natural next test is whether CoCoMix's advantage persists when the pause token is given the same compute or when the concept vector is generated by the student itself.
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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

3 major / 5 minor

Summary. The paper proposes CoCoMix, a pretraining framework that augments next-token prediction with a concept-prediction loss derived from a frozen pretrained GPT-2 and a TopK sparse autoencoder (SAE). For each token position, the top K_attr concepts by a gradient-times-activation attribution score are used as discrete labels for a linear concept head on the student; the predicted concepts are then sparsified, compressed into a continuous vector, and interleaved into the student's hidden-state sequence. Experiments on 69M, 386M, and 1.38B models pretrained on OpenWebText report improved validation perplexity and downstream accuracy relative to NTP, knowledge distillation, and pause-token baselines, including a headline claim of matching NTP's final perplexity with 21.5% fewer training tokens at 1.38B. The paper also demonstrates qualitative concept steering and claims weak-to-strong supervision benefits.

Significance. If the results hold under a compute-normalized comparison, CoCoMix is a valuable contribution: it shows that SAE-derived concept targets can provide a training signal beyond token-level loss, and the component ablations in Fig. 6 usefully isolate concept prediction from concept interleaving. The public code link and the pause-token control (which pays a similar doubled-sequence cost) are strengths that partially address the extra-compute confound. However, the headline sample-efficiency claim currently rests on token counts only and on apparently single runs, so the significance is conditional pending FLOP- or wall-clock-normalized evidence and variance reporting.

major comments (3)
  1. [§3.1, Fig. 2a; §2.2; Appendix A] The central claim that CoCoMix is 'more sample efficient' and reaches NTP's final validation perplexity with 21.5% fewer tokens is based on token counts, not compute. Because the compressed concept vector ĉ_t is interleaved after every token (Eq. 3) and the insertion point for the 1.38B model is layer 6 of 24, layers 7–24 process sequences of length 2T instead of T; each such layer therefore incurs roughly 2× the MLP cost and up to 4× the attention cost, offset only slightly by the smaller hidden dimension (d=2028 vs d=2096 for the baseline). At the claimed crossing point near 157B tokens, CoCoMix has plausibly consumed substantially more cumulative FLOPs than NTP at 200B tokens. The sentence on p.5—'CoCoMix utilizes fewer FLOPs than Pause token … but more FLOPs than NTP'—is the only disclosure and is not quantified. Please add FLOP- or wall-clock-normalized learning curves, or an iso-compute comparison, and state whether the teacher forward/backward pass for computing concept labels is included in the cost; otherwise the efficiency claim should be restated as 'token efficiency at fixed perplexity' rather than sample efficiency.
  2. [Table 1, Figs. 2–4] No error bars, random seeds, or significance tests are reported, and the headline 1.38B result appears to be a single run. Several reported differences are small and inconsistent across tasks: for example, at 69M in Table 1, CoCoMix is slightly worse than NTP on ARC-Easy (39.3 vs 39.7) and effectively ties on PIQA and HellaSwag; at 386M, PIQA is a tie. Given the language in §3.1 that improvements are 'consistent and significant,' the paper should either report variance over multiple seeds or temper this claim. This is load-bearing because the 'consistently outperforms' statement is a central contribution of the paper.
  3. [§2.2, Appendix A] All experiments use a single fixed teacher/SAE configuration: one open-source TopK SAE with K_concept=32 and C=32768, a 124M GPT-2 teacher, extraction layer L_con=6, K_attr=4, and λ=0.1. No sensitivity analysis is provided over SAE sparsity, K_attr, teacher size, or insertion layer. Since the concept-prediction targets are entirely defined by this teacher and SAE, the reported gains could be idiosyncratic to that particular choice. A sweep over K_attr and SAE sparsity on the 69M/20B setup, plus at least one different teacher size or SAE variant, would substantially strengthen the claim that concept-level supervision—rather than a specific SAE configuration—is responsible for the improvements.
minor comments (5)
  1. [Eq. (1)–(3)] Please clarify the gradient variable in Eq. (1): the notation suggests ∇_{c_t} while the input multiplier is cpre_t, and it is unclear whether gradients through the TopK nonlinearity are included. Also specify the K used in the TopK of Eq. (3) (K_attr? K_concept? something else), and state how positional information is handled for the interleaved concept vectors.
  2. [Section 3, Appendix A] The 'activated parameter count' matching is not fully defined: please state explicitly which parameters are counted for CoCoMix (concept predictor, compression layer, inserted vectors) and how the baseline hidden dimensions are chosen. The FLOP contribution of the concept-target computation should also be reported, since it is part of CoCoMix's training cost.
  3. [Table 1 caption] The caption says 'We report performance on the OpenWebText (OWT) training set,' but the evaluation section describes validation perplexity and Fig. 2a labels the metric as validation perplexity. Please correct the caption.
  4. [Fig. 6b] For the direct hidden-state prediction baselines, please specify the compression mechanism used to turn the predicted full hidden state into the inserted continuous embedding; the caption currently says only that the predicted representation is 'compressed into a continuous embedding,' which is not enough to ensure the comparison is architectural.
  5. [Figures 1, 5, 10] The text in several figures appears as garbled glyphs in the submitted version. Please ensure that fonts and special characters render correctly in the final PDF.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: concept targets come from a frozen external teacher and SAE, and the concept-prediction loss is an additive side objective; the token-efficiency concern is a FLOPs normalization issue, not a circularity.

full rationale

The derivation chain is self-contained. In Eq. (1), the concept labels are selected from a_t = cpre_t ⊙ ∇_{ct} −log fcon(x_{t+1} | D(c_t), h_<t), which depends only on the frozen 124M GPT-2 teacher and an externally released TopK SAE (Gao et al., 2024), not on the student M or its parameters. Eq. (2) is an additive cross-entropy regularizer over those fixed label indices, and Eq. (4) adds it to the standard NTP loss, so no quantity being predicted is defined in terms of the student's own output. The interleaved vector in Eq. (3), ĉ_t = W TopK(z_t) + b, is produced from the student's predicted logits, but it is trained against external teacher concepts; this is an auxiliary task plus architectural self-conditioning, not a fitted-input-renamed-as-prediction. The overlapping-author citations (Hao et al., 2024; Lanchantin et al., 2024) are motivational and related-work, and they carry no load-bearing premise. The skeptical FLOPs concern about the 21.5% token-savings claim is a legitimate evaluation-normalization question, but it is not circularity: the token comparison is not equivalent by construction to the concept-prediction objective. No step in the claimed derivation reduces to its own input.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

CoCoMix introduces no new physical or mathematical entities; the 'continuous concept' is a linear compression (W, b in Eq. 3) of the student's predicted TopK concept logits. The method rests on three domain assumptions: that SAE features are meaningful concepts (assumed from prior SAE literature), that attribution scores identify the most useful concepts for the teacher's prediction (used in Eq. 1 to build labels), and that the student's hidden state contains enough information to predict these concepts (Eq. 2). The hyperparameters lambda, K_attr, insertion layer, and teacher layer are chosen by hand, not fitted to the test set. No circular fit to the target result is present, since labels come from a frozen teacher and evaluation is on downstream tasks.

free parameters (5)
  • lambda (concept loss weight) = 0.1
    Scales the concept prediction loss in Eq. 4; chosen by hand, no sensitivity analysis reported.
  • K_attr = 4
    Number of top attribution concepts used as labels per position (Eq. 2).
  • K_concept = 32
    Sparsity of the pretrained TopK SAE; inherited from the open-source SAE (Gao et al., 2024), not tuned by the paper.
  • teacher layer L_con = 6
    Layer of the 124M GPT-2 from which hidden states are fed to the SAE; chosen as the middle layer.
  • student insertion layer = 4 (69M), 6 (386M, 1.38B)
    Layer where concepts are predicted and interleaved; chosen to balance early vs. late fusion.
assumptions (4)
  • domain assumption SAE features correspond to semantically meaningful concepts
    The paper relies on prior Sparse Autoencoder literature to treat each activated dimension of the TopK SAE as a discrete concept; used throughout Section 2.1.
  • domain assumption Gradient x input attribution identifies concepts causally relevant to next-token prediction
    Eq. 1 defines the attribution score as preactivation times loss gradient; the paper selects top-K concepts by this score without proof that this choice is optimal.
  • domain assumption The student can predict teacher concepts from its own hidden state
    Eq. 2 assumes the student's hidden state h_t at layer L_in contains enough information to predict the teacher's selected concept indices; if not, the concept loss would be uninformative.
  • domain assumption The SAE decoder reconstruction is a sufficient statistic of the teacher hidden state
    Attribution uses f_con(D(c_t), h_{<t}) with reconstruction D(c_t), so the decoder's fidelity determines whether attribution is accurate.

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Pith. "Pith review of LLM Pretraining with Continuous Concepts." pith.science (2026). https://pith.science/paper/32AGNVES

@misc{pith2026250208524,
  author       = {Pith},
  title        = {Pith review of: LLM Pretraining with Continuous Concepts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/32AGNVES}},
  note         = {Machine review of arXiv:2502.08524}
}
read the original abstract

Next token prediction has been the standard training objective used in large language model pretraining. Representations are learned as a result of optimizing for token-level perplexity. We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with continuous concepts. Specifically, CoCoMix predicts continuous concepts learned from a pretrained sparse autoencoder and mixes them into the model's hidden state by interleaving with token hidden representations. Through experiments on multiple benchmarks, including language modeling and downstream reasoning tasks, we show that CoCoMix is more sample efficient and consistently outperforms standard next token prediction, knowledge distillation and inserting pause tokens. We find that combining both concept learning and interleaving in an end-to-end framework is critical to performance gains. Furthermore, CoCoMix enhances interpretability and steerability by allowing direct inspection and modification of the predicted concept, offering a transparent way to guide the model's internal reasoning process.

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Forward citations

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Reference graph

Works this paper leans on

31 extracted references · 7 canonical work pages · cited by 9 Pith papers

  1. [3]

    Think you have solved question answering? try arc, the ai2 reasoning challenge.arXiv preprint arXiv:1803.05457,

    Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge.arXiv preprint arXiv:1803.05457,

    arXiv

  2. [5]

    Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437,

    DeepSeek-AI. Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437,

    arXiv

  3. [7]

    The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783,

    arXiv

  4. [8]

    Assistgpt: A general multi-modal assistant that can plan, execute, inspect, and learn.arXiv preprint arXiv:2306.08640,

    Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, and Mike Zheng Shou. Assistgpt: A general multi-modal assistant that can plan, execute, inspect, and learn.arXiv preprint arXiv:2306.08640,

    arXiv

  5. [9]

    Scaling and evaluating sparse autoencoders.arXiv preprint arXiv:2406.04093,

    Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders.arXiv preprint arXiv:2406.04093,

    arXiv

  6. [10]

    Miniplm: Knowledge distillation for pre-training language models

    Yuxian Gu, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. Miniplm: Knowledge distillation for pre-training language models. arXiv preprint arXiv:2410.17215,

    arXiv

  7. [11]

    Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769,

    Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769,

    arXiv

  8. [12]

    Distilling the knowledge in a neural network

    Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531,

    arXiv

Show all 31 references
  1. [13]

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

    LCM, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R Costa-jussà, et al. Large concept models: Language modeling in a sentence representation space.arXiv preprint arXiv:2412.08821,

    arXiv

  2. [14]

    Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.arXiv preprint arXiv:2408.05147,

    Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.arXiv preprint arXiv:2408.05147,

    arXiv

  3. [15]

    Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101,

    I Loshchilov. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101,

    arXiv

  4. [20]

    Code llama: Open foundation models for code.arXiv preprint arXiv:2308.12950,

    Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, et al. Code llama: Open foundation models for code.arXiv preprint arXiv:2308.12950,

    arXiv

  5. [21]

    Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556,

    12 Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556,

    arXiv

  6. [22]

    Gemma 2: Improving open language models at a practical size.arXiv preprint arXiv:2408.00118,

    Gemma Team. Gemma 2: Improving open language models at a practical size.arXiv preprint arXiv:2408.00118,

    arXiv

  7. [23]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al

    https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information...

    2024

  8. [24]

    Evaluation of chatgpt and microsoft bing ai chat performances on physics exams of vietnamese national high school graduation examination.arXiv preprint arXiv:2306.04538,

    Dao Xuan-Quy, Le Ngoc-Bich, Phan Xuan-Dung, Ngo Bac-Bien, and Vo The-Duy. Evaluation of chatgpt and microsoft bing ai chat performances on physics exams of vietnamese national high school graduation examination.arXiv preprint arXiv:2306.04538,

    arXiv

  9. [25]

    Distilling system 2 into system 1.arXiv preprint arXiv:2407.06023,

    Ping Yu, Jing Xu, Jason Weston, and Ilia Kulikov. Distilling system 2 into system 1.arXiv preprint arXiv:2407.06023,

    arXiv

  10. [26]

    Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors.arXiv preprint arXiv:2103.15949,

    Zeyu Yun, Yubei Chen, Bruno A Olshausen, and Yann LeCun. Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors.arXiv preprint arXiv:2103.15949,

    arXiv

  11. [27]

    Representation engineering: A top-down approach to ai transparency

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405,

    arXiv

  12. [28]

    Our method and baseline both utilize the GPT-2-based Transformer architecture and tokenizer (Radford et al., 2019), with a context length of

    13 Appendix A Experimental Details Architecture details. Our method and baseline both utilize the GPT-2-based Transformer architecture and tokenizer (Radford et al., 2019), with a context length of

    2019

  13. [30]

    Consequently, our models introduce an additional(C + Kconcept) × d activated parameters on top of the base Transformer parameters

    for concept extraction, whereKconcept is set to 32 and the concept activation size is 32,768. Consequently, our models introduce an additional(C + Kconcept) × d activated parameters on top of the base Transformer parameters. The GPT-2 model (a teacher model for KD and a concep...

    2020

  14. [31]

    Latent concept modeling is a

    These experiments confirmed that the model could effectively modulate its output based on these newly identified concepts, producing coherent and concept-aligned generations. Next, to verify whether the 14 NTP CoCoMix (ours) Validation Perplexity 22 24 26 28 Training tokens (B...

  15. [1024]

    For CoCoMix, the hidden state dimensionsd are 512, 1024, and 2028, with 8, 24, and 24 layers, respectively

    We consider three model sizes, defined by the number of activated parameters: 69M, 386M, and 1.38B. For CoCoMix, the hidden state dimensionsd are 512, 1024, and 2028, with 8, 24, and 24 layers, respectively. The number of attention heads is set to 8, 16, and 16 for the three c...

  16. [2014]

    Sparse feature circuits: Discovering and editing interpretable causal graphs in language models.arXiv preprint arXiv:2403.19647,

    Samuel Marks, Can Rager, Eric J Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller. Sparse feature circuits: Discovering and editing interpretable causal graphs in language models.arXiv preprint arXiv:2403.19647,

    arXiv

  17. [2016]

    Openwebmath: An open dataset of high-quality mathematical web text.arXiv preprint arXiv:2310.06786,

    Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, and Jimmy Ba. Openwebmath: An open dataset of high-quality mathematical web text.arXiv preprint arXiv:2310.06786,

    arXiv

  18. [2017]

    Show your work: Scratchpads for intermediate computation with language models.arXiv preprint arXiv:2112.00114,

    Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. Show your work: Scratchpads for intermediate computation with language models.arXiv preprint arXiv:2112.00114,

    arXiv

  19. [2018]

    Sparse autoencoders find highly interpretable features in language models.arXiv preprint arXiv:2309.08600,

    Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. Sparse autoencoders find highly interpretable features in language models.arXiv preprint arXiv:2309.08600,

    arXiv

  20. [2019]

    A little help goes a long way: Efficient llm training by leveraging small lms.arXiv preprint arXiv:2410.18779,

    Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, et al. A little help goes a long way: Efficient llm training by leveraging small lms.arXi...

    arXiv

  21. [2020]

    Weak-to-strong generalization: Eliciting strong capabilities with weak supervision

    Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al. Weak-to-strong generalization: Eliciting strong capabilities with weak supervision. arXiv preprint arXiv:2312.09390,

    arXiv

  22. [2023]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    https://transformer-circuits.pub/2023/monosemantic- features/index.html. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. InAd...

    2023

  23. [2024]

    Implicit chain of thought reasoning via knowledge distillation.arXiv preprint arXiv:2311.01460,

    Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, and Stuart Shieber. Implicit chain of thought reasoning via knowledge distillation.arXiv preprint arXiv:2311.01460,

    arXiv

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Reviewed August 8, 2026 · model on record in the stance chip above.