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REVIEW 5 major objections 6 minor 25 references

Data-driven inventory management for new products: An adjusted Dyna-$Q$ approach with transfer learning

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read An adjusted Dyna-Q algorithm with transfer learning cuts new-product inventory cost by up to 23.7% and training time by up to 77.5% in a bakery case study.

desk verdict The paper's own Table III contradicts its headline claim of lowest variance for the adjusted Dyna-Q with transfer, and the 'real data' validation is really a synthetic Gamma demand test. read the letter →

arxiv 2501.08109 v4 pith:NDYGYQU2 submitted 2025-01-14 cs.LG cs.AIcs.CE

Xinye Qu , Longxiao Liu , Wenjie Huang This is my paper · ORCID
classification cs.LGcs.AIcs.CE
keywords inventorymanagementreinforcementlearningDyna-Qtransfercold-startproblemBayesianneuralnetworkperishablegoodsQ-learning
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

This paper aims to show that inventory policies for newly launched products—where no demand history exists—can be learned quickly and at lower cost by an adjusted Dyna-Q algorithm that combines model-free and model-based reinforcement learning and is warm-started with transfer learning. In a bakery case study, the adjusted Dyna-Q reduces average daily testing cost by up to 23.7% against Q-learning and shortens per-episode training time by up to 77.5% against classic Dyna-Q. Adding transfer learning from a similar existing product lowers the variance of total 30-day cost and achieves the best average total cost among the benchmarks. If correct, this gives retailers a practical recipe for setting ordering rules immediately after a product launch instead of waiting for demand history.

What carries the argument

The load-bearing object is the adjusted Dyna-Q algorithm, a variant of Dyna-Q in which the exploration probability $\varepsilon_t$ and the number of planning steps $N_t$ are both updated by the search-then-convergence schedule $\varepsilon_t = \max\{\varepsilon_0/(1+y), \varepsilon_{\min}\}$ and $N_t = \max\{N_0/(1+y), N_{\min}\}$ with $y = t^2/(\partial+t)$, so that the agent explores heavily at first and gradually shifts toward cheap model-free updates. Around it, the paper builds a model $M(s,a)$ from two neural networks—one predicting state transitions and one predicting cost—and a transfer-learning layer in which Q-learning is first run on an offline simulated demand set produced by a three-layer Bayesian neural network trained on the similar product's sales; the resulting Q-table $Q_D$ and model $M_D$ initialize the Dyna-Q agent. This machinery is what lets the agent learn from both simulated and real experience while correcting for the unavoidable mismatch between the model and the new product's true demand.

What would settle it

Re-run the 30-day training-and-testing protocol on a demand distribution that is not Gamma (for example, an empirical demand histogram from a different bakery's new product, or a negative-binomial distribution with the same mean and variance) and check whether adjusted Dyna-Q with transfer learning still produces the lowest average total cost and lowest variance.

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Extended reading notes

Core claim

The paper's central claim is that its adjusted Dyna-Q with transfer learning is the best of the tested algorithms for cold-start perishable inventory management: in 30-day out-of-sample testing it records the lowest average total cost (65.74) and the lowest total-cost variance (93.8) among Q-learning, classic Dyna-Q, and adjusted Dyna-Q, while keeping the shortage percentage at a relatively low 0.227. The authors attribute this to two mechanisms: the STC-decayed exploration and planning steps, which prevent model mismatch from accumulating, and the transfer of a Q-table and environment model estimated from similar-product demand, which stabilizes the early training phase. They also report that the same algorithm without transfer learning still beats the classic benchmarks, and that Bayesian neural network environment models outperform multilayer perceptron models on cost.

Load-bearing premise

The result rests on the assumption that the new product's daily demand really is well approximated by a discretized Gamma distribution with mean 5 and variance 1, 3, or 5, and that the similar product's sales data used for transfer learning are informative about that demand; if real new-product demand departs from this, the reported cost and training-time gains may not carry over.

Editorial extensions

If this is right

  • A retailer can form an ordering policy for a new perishable product within one month of launch instead of waiting for a long demand history.
  • Transfer learning reduces the variability of total monthly cost across repeated trials, so early policy decisions are more reliable.
  • Decaying the number of planning steps makes Dyna-Q computationally tractable, cutting per-episode training time by about three-quarters.
  • The resulting policy holds more inventory on average, which lowers shortage percentages but raises holding cost—a trade-off managers can tune through cost parameters.

Reading between the lines

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

  • Beyond the paper, the STC decay schedule could be transferred to other Dyna-style agents whose learned environment models are unreliable in early training, regardless of inventory context.
  • The paper does not quantify how sensitive the warm-start benefit is to the similarity between source and target products; a natural extension would be to vary the similarity of the source product and measure the variance reduction.
  • An obvious next comparison is a classical newsvendor or base-stock policy fitted to the same Gamma demand: that would separate the gain attributable to reinforcement learning from the gain attributable to the demand forecast 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

5 major / 6 minor

Summary. The paper proposes an adjusted Dyna-Q algorithm for inventory management of newly launched products with no historical demand data, combining model-free Q-learning with a learned model and using a search-then-convergence (STC) schedule to decay exploration and planning steps. It also introduces transfer learning from demand data of a similar existing product (Boule 200g) to warm-start both the Q-table and the model for the new product (Boule 400g). The manuscript reports a bakery case study in which the adjusted Dyna-Q reduces average daily cost by up to 23.7% versus Q-learning and training time by up to 77.5% versus classic Dyna-Q, and claims that with transfer learning it achieves the lowest total cost, lowest variance, and relatively low shortage percentages in a 30-day testing scenario.

Significance. If the empirical claims held, the paper would provide a practical cold-start inventory policy that blends the sample efficiency of model-based RL with the robustness of model-free Q-learning. The combination of Dyna-Q with a decaying planning schedule and the transfer of both a Q-table and a model from a source product is a useful and moderately novel idea. The manuscript provides a concrete algorithmic recipe in Algorithm 1, uses a publicly available bakery dataset for the transfer source, and compares against classic Dyna-Q and Q-learning on cost, shortage, variance, and training time. However, the central validation is weakened by the use of a simulated test environment and by an internal inconsistency in the headline variance claim; both issues need to be resolved before the findings can be taken as stated.

major comments (5)
  1. [Abstract and Table III] The abstract claims that the adjusted Dyna-Q with transfer learning has "the lowest total cost, lowest variance in total cost, and relatively low shortage percentages among all the benchmarking algorithms under a 30-day testing." This is not supported by the paper's own Table III. In Scenario 2, the variance in total cost for adjusted Dyna-Q with transfer is 93.8, while Dyna-Q with transfer reports 30.8 and Q-learning reports 21.2; adjusted Dyna-Q with transfer is therefore third among the five configurations. Its shortage percentage (0.227) also exceeds that of Q-learning (0.150). If the intended claim is only that transfer reduces variance relative to the same algorithm without transfer, that is supported (93.8 vs. 250.2 for adjusted Dyna-Q, and 30.8 vs. 133.3 for classic Dyna-Q), but the abstract's stronger statement must be corrected.
  2. [Section III.B] The reported case study is not a real-data validation of the final ordering policy. The daily demand for Boule 400g is assumed to follow a discretized Gamma distribution with mean 5 and variance 1, 3, or 5, and the same distribution is used for both training and testing. The only real data from the bakery (Boule 200g) enters through the transfer-learning model; the 23.7% cost reduction and 77.5% training-time reduction are measured in a simulator whose demand model is the authors' assumption. Moreover, the source product's mean (4.48) is close to the assumed new-product mean (5), so the evaluation does not test robustness to distribution shift. To justify the abstract's phrase "validated through a case study ... with real data," the authors should evaluate on a held-out real demand sequence (e.g., a later period of Boule 200g sales treated as the new product) or add a misspecification analysis in which the test demand differs from the training demand distribution. Without one of these, the case study should be described as a simulation calibrated to real data.
  3. [Section III.B and Table III] The averages in Table III are based on 100 test repetitions, but no standard errors or confidence intervals are reported. Given variance values such as 250.2 (adjusted Dyna-Q without transfer) and 93.8 (adjusted Dyna-Q with transfer), the gap between the best average total cost (65.74) and the next adjusted variant (69.91) may not be statistically meaningful. The authors should report standard errors or bootstrap intervals and, where possible, a paired significance test over the 100 episodes before asserting that adjusted Dyna-Q with transfer "performs the best on testing."
  4. [Section II.A] The STC schedule introduces six hyperparameters (ε0, εmin, ∂ε, N0, Nmin, ∂N) in addition to the learning rate α and discount γ, and no sensitivity analysis is provided. Since the decaying exploration and planning schedules are the paper's main algorithmic innovation, the reported advantages over classic Dyna-Q could depend on these choices. The authors should report the sensitivity of the Table I and Table III results to the STC parameters, or at least justify the chosen values with a small grid search.
  5. [Section II.B] The construction of the offline simulated demand dataset D is underspecified. It is not stated whether the BNN output dhat_i is a point forecast or a posterior predictive sample, nor what mean and variance the generated data actually has. Because the transferred Q-table and model depend on D, this omission hinders reproducibility and makes it difficult to assess how similar the source product is to the new product. The authors should specify the sampling procedure and report summary statistics of D.
minor comments (6)
  1. [Eq. (4)] In the second state transition equation, "ss_t" should read "s2_t."
  2. [Table I] The "Cost improvement" column should state explicitly that the comparison basis is Q-learning's average daily cost for the same dataset; as written, the "—" entry for adjusted Dyna-Q in the σ²=1, MLP row is unclear because its cost is higher than Q-learning.
  3. [Figure 3] The statement that adjusted Dyna-Q with transfer learning gives the transition probability closest to the true probability is based on visual inspection; the authors should report a numeric error, for example the absolute deviation from the true probability of 0.12.
  4. [Section III.B] The 77.5% training-time improvement is only relative to classic Dyna-Q; Table I shows that Q-learning is much faster (0.50 seconds per episode) because it performs no planning, so the paper should qualify all training-time statements accordingly.
  5. [Section IV] The future-work item about measuring similarity between existing and new products acknowledges that the transfer step currently assumes rather than tests similarity; this assumption should also be stated explicitly in Section II.B where the transfer is introduced.
  6. [General] The paper would benefit from reporting the number of random seeds used for Table I and from releasing the source code, as this would materially improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the algorithm is implemented and tested against an assumed demand model rather than derived from its own conclusions; the Table III variance inconsistency is a correctness issue, not a circularity.

full rationale

The paper's derivation chain is not circular. The adjusted Dyna-Q algorithm is a standard Dyna-Q variant with Q-learning updates, an STC-decayed exploration probability, and a decaying planning step count. The transfer-learning variant obtains its warm-start Q-table and model by training a Bayesian neural network on historical sales of an existing product (Boule 200g) before interacting with the new-product environment. The target environment is a synthetic Gamma demand process with mean 5 and variance 1, 3, or 5, chosen after inspecting the Boule 200g mean of 4.48, so the transfer source and target are deliberately similar; however, this is an experimental-design and favorable-evaluation choice, not an equation that reduces a predicted quantity to a fitted input by construction. No load-bearing claim rests on a self-citation: the cited prior work on STC, transfer Q-learning, and model-based RL is external, and no uniqueness theorem from the same authors is invoked to force the algorithm choice. The abstract's claim that transfer-adjusted Dyna-Q has the 'lowest variance in total cost' is contradicted by Table III (variance 93.8 vs 30.8 for Dyna-Q with transfer and 21.2 for Q-learning), but this is an internal-consistency and correctness problem, not circularity. Under the requested rubric, the honest finding is no significant circularity and score 0.

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

Everything the reported improvements depend on is either a hand-picked hyperparameter, an assumed cost structure, or a simulation of demand. The only external data is the Boule 200g demand used for warm-start transfer. The synthetic demand assumption is the most consequential: without it there is no new-product environment at all.

free parameters (4)
  • STC schedule hyperparameters = epsilon0=0.4, epsilon_min=0.1, N0=100, N_min=10, d_epsilon=7500, d_N=5000 (Table I); different values in Scenarios 1…
    Chosen by hand without sensitivity analysis; directly control the balance of exploration and planning, so results may depend on these choices.
  • RL learning hyperparameters = alpha=0.3, gamma=0.9 in Table I; alpha=0.1, gamma=0.9 in transfer scenarios
    Chosen without a tuning protocol; different values could change rankings.
  • Unit cost parameters = [b1,b2,b3]=[0.7,0.3,0], Cs=1 Euro
    Assumed costs; every reported total cost depends linearly on these values.
  • New product demand distribution = Gamma with mean 5, variances 1, 3, 5, discretized on [0,10]
    This is a modeling choice, not data; it sets the environment in which all algorithms are compared.
assumptions (4)
  • domain assumption The daily demand of Boule 400g follows a discretized Gamma distribution with mean 5 and variance 1, 3, or 5.
    Section III.B states this directly; the entire evaluation environment is synthetic.
  • ad hoc to paper Boule 200g demand is sufficiently similar to Boule 400g demand to make transfer learning beneficial.
    The similarity is asserted, and the chosen mean 5 is close to the observed 4.48, making the transfer setting favorable.
  • domain assumption Immediate replenishment, unlimited supplier capacity, zero ordering cost, FIFO sales, and 3-day shelf life.
    Standard single-echelon perishable inventory assumptions; if violated in practice, the MDP is different.
  • domain assumption The neural network model M(s,a) can serve as an accurate simulator for planning.
    The BNN/MLP model is fit to limited experience; the paper does not quantify model error across all state-action pairs.

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Cite this review

Pith. "Pith review of Data-driven inventory management for new products: An adjusted Dyna-$Q$ approach with transfer learning." pith.science (2026). https://pith.science/paper/NDYGYQU2

@misc{pith2026250108109,
  author       = {Pith},
  title        = {Pith review of: Data-driven inventory management for new products: An adjusted Dyna-$Q$ approach with transfer learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NDYGYQU2}},
  note         = {Machine review of arXiv:2501.08109}
}
abstract

In this paper, we propose a novel reinforcement learning algorithm for inventory management of newly launched products with no historical demand information. The algorithm follows the classic Dyna-$Q$ structure, balancing the model-free and model-based approaches, while accelerating the training process of Dyna-$Q$ and mitigating the model discrepancy generated by the model-based feedback. Based on the idea of transfer learning, warm-start information from the demand data of existing similar products can be incorporated into the algorithm to further stabilize the early-stage training and reduce the variance of the estimated optimal policy. Our approach is validated through a case study of bakery inventory management with real data. The adjusted Dyna-$Q$ shows up to a 23.7\% reduction in average daily cost compared with $Q$-learning, and up to a 77.5\% reduction in training time within the same horizon compared with classic Dyna-$Q$. By using transfer learning, it can be found that the adjusted Dyna-$Q$ has the lowest total cost, lowest variance in total cost, and relatively low shortage percentages among all the benchmarking algorithms under a 30-day testing.

Figures

Figures reproduced from arXiv: 2501.08109 by the authors.

Figure 1
Figure 1. Pipeline of adjusted Dyna-Q with transfer learning For our problem, two state transition processes will occur: one at the beginning of the period and the other at the end associated with customer demand. The first state transition is elaborated in the formula as s 1 t = s 2 t−1 , s 2 t = s 3 t−1 , s 3 t = at−1, which summarizes that the shelf-life of all products will decrease by one due to decaying. Then the st wil… view at source ↗
Figure 2
Figure 2. Convergence of adjusted Dyna-Q algorithm (“Var” represents the variance σ 2 ) compare the proposed algorithm with Q-learning and classic Dyna-Q as benchmarks. All three algorithms go through the same training phase with horizon T = 100 days. Then, the trained Q-value and estimated optimal policy are applied for testing in the same environment, with the average daily cost within another 100 days. The performance comp… view at source ↗
Figure 3
Figure 3. Transition probability comparison IV. CONCLUSION The paper presents a new RL algorithm for inventory management of the newly launched products where historical demand information is unavailable. The proposed adjusted Dyna-Q algorithm is built upon the classic Dyna-Q algorithm, aiming to strike a balance between model-based and model-free approaches during training. Additionally, based on the idea of transfer learnin… view at source ↗

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Reference graph

Works this paper leans on

25 extracted references · 21 canonical work pages

  1. [1]

    Optimal ordering, issuance and disposal policies for inventory management of perishable products,

    R. Haijema, “Optimal ordering, issuance and disposal policies for inventory management of perishable products,” International Journal of Production Economics , vol. 157, pp. 158–169, 2014

    work page 2014

  2. [2]

    Addressing the cold-start problem of recommendation systems for financial products by using few-shot deep learning,

    T.-Y . Hung and S.-H. Huang, “Addressing the cold-start problem of recommendation systems for financial products by using few-shot deep learning,” Applied Intelligence , vol. 52, no. 13, pp. 15 529–15 546, 2022

    work page 2022

  3. [3]

    Increasing supply chain robustness through process flexibility and inventory,

    D. Simchi-Levi, H. Wang, and Y . Wei, “Increasing supply chain robustness through process flexibility and inventory,” Production and Operations Management, vol. 27, no. 8, pp. 1476–1491, 2018

    work page 2018

  4. [4]

    C. C. Poirier and S. E. Reiter, Supply chain optimization: building the strongest total business network . Berrett-Koehler Publishers, 1996

    work page 1996

  5. [5]

    Inventory management in supply chains: a reinforcement learning approach,

    I. Giannoccaro and P. Pontrandolfo, “Inventory management in supply chains: a reinforcement learning approach,” International Journal of Production Economics, vol. 78, no. 2, pp. 153–161, 2002

    work page 2002

  6. [6]

    Global supply chain management: a reinforcement learning approach,

    P. Pontrandolfo, A. Gosavi, O. G. Okogbaa, and T. K. Das, “Global supply chain management: a reinforcement learning approach,” International Journal of Production Research, vol. 40, no. 6, pp. 1299– 1317, 2002

    work page 2002

  7. [7]

    Inventory management of new products in retailers using model-based deep reinforcement learning,

    T. Demizu, Y . Fukazawa, and H. Morita, “Inventory management of new products in retailers using model-based deep reinforcement learning,” Expert Systems with Applications, vol. 229, p. 120256, 2023

    work page 2023

  8. [8]

    Deep reinforcement learning for inventory control: A roadmap,

    R. N. Boute, J. Gijsbrechts, W. Van Jaarsveld, and N. Vanvuchelen, “Deep reinforcement learning for inventory control: A roadmap,” European Journal of Operational Research , vol. 298, no. 2, pp. 401– 412, 2022

    work page 2022

Show all 25 references
  1. [9]

    Offline meta- level model-based reinforcement learning approach for cold-start recommendation,

    Y . Wang, Y . Ge, L. Li, R. Chen, and T. Xu, “Offline meta- level model-based reinforcement learning approach for cold-start recommendation,” arXiv preprint arXiv:2012.02476 , 2020

    2012 arXiv

  2. [10]

    Dyna, an integrated architecture for learning, planning, and reacting,

    R. S. Sutton, “Dyna, an integrated architecture for learning, planning, and reacting,” ACM Sigart Bulletin , vol. 2, no. 4, pp. 160–163, 1991

    1991

  3. [11]

    An improved dyna-q algorithm for mobile robot path planning in unknown dynamic environment,

    M. Pei, H. An, B. Liu, and C. Wang, “An improved dyna-q algorithm for mobile robot path planning in unknown dynamic environment,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 52, no. 7, pp. 4415–4425, 2021

    2021

  4. [12]

    Pseudo dyna-q: A reinforcement learning framework for interactive recommendation,

    L. Zou, L. Xia, P. Du, Z. Zhang, T. Bai, W. Liu, J.-Y . Nie, and D. Yin, “Pseudo dyna-q: A reinforcement learning framework for interactive recommendation,” in Proceedings of the 13th International Conference on Web Search and Data Mining , 2020, pp. 816–824

    2020

  5. [13]

    A survey of transfer learning,

    K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” Journal of Big data , vol. 3, pp. 1–40, 2016

    2016

  6. [14]

    Imitation and transfer q-learning-based parameter identification for composite load modeling,

    J. Xie, Z. Ma, K. Dehghanpour, Z. Wang, Y . Wang, R. Diao, and D. Shi, “Imitation and transfer q-learning-based parameter identification for composite load modeling,” IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1674–1684, 2020

    2020

  7. [15]

    Target transfer q-learning and its convergence analysis,

    Y . Wang, Y . Liu, W. Chen, Z.-M. Ma, and T.-Y . Liu, “Target transfer q-learning and its convergence analysis,” Neurocomputing, vol. 392, pp. 11–22, 2020

    2020

  8. [16]

    Transferring models in hybrid reinforcement learning agents,

    A. Fachantidis, I. Partalas, G. Tsoumakas, and I. Vlahavas, “Transferring models in hybrid reinforcement learning agents,” in International Conference on Engineering Applications of Neural Networks. Springer, 2011, pp. 162–171

    2011

  9. [17]

    q-transfer: A novel framework for efficient deep transfer learning in networking,

    T. V . Phan, S. Sultana, T. G. Nguyen, and T. Bauschert, “ q-transfer: A novel framework for efficient deep transfer learning in networking,” in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) . IEEE, 2020, pp. 146–151

    2020

  10. [18]

    Learning rate schedules for faster stochastic gradient search,

    C. Darken, J. Chang, J. Moody et al. , “Learning rate schedules for faster stochastic gradient search,” in Neural Networks for Signal Processing, vol. 2. Citeseer, 1992, pp. 3–12

    1992

  11. [19]

    Survey of model-based reinforcement learning: Applications on robotics,

    A. S. Polydoros and L. Nalpantidis, “Survey of model-based reinforcement learning: Applications on robotics,” Journal of Intelligent & Robotic Systems , vol. 86, no. 2, pp. 153–173, 2017

    2017

  12. [20]

    Plan online, learn offline: Efficient learning and exploration via model-based control,

    K. Lowrey, A. Rajeswaran, S. Kakade, E. Todorov, and I. Mordatch, “Plan online, learn offline: Efficient learning and exploration via model-based control,” arXiv preprint arXiv:1811.01848 , 2018

    2018 arXiv

  13. [21]

    Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,

    Y . Luo, H. Xu, Y . Li, Y . Tian, T. Darrell, and T. Ma, “Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,” arXiv preprint arXiv:1807.03858 , 2018

    2018 arXiv

  14. [22]

    Hands-on bayesian neural networks–a tutorial for deep learning users,

    L. V . Jospin, H. Laga, F. Boussaid, W. Buntine, and M. Bennamoun, “Hands-on bayesian neural networks–a tutorial for deep learning users,” IEEE Computational Intelligence Magazine , vol. 17, no. 2, pp. 29–48, 2022

    2022

  15. [23]

    French bakery daily sales,

    M. Gimbert, “French bakery daily sales,” Nov

  16. [24]

    Multilayer perceptron and neural networks,

    M.-C. Popescu, V . E. Balas, L. Perescu-Popescu, and N. Mastorakis, “Multilayer perceptron and neural networks,” WSEAS Transactions on Circuits and Systems , vol. 8, no. 7, pp. 579–588, 2009

    2009

  17. [2022]

    Available: https://www.kaggle.com/datasets/ matthieugimbert/french-bakery-daily-sales

    [Online]. Available: https://www.kaggle.com/datasets/ matthieugimbert/french-bakery-daily-sales

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Reviewed August 10, 2026 · model on record in the stance chip above.