arxiv: 2604.05341 · v1 · submitted 2026-04-07 · 💻 cs.IR

Recognition: 2 theorem links

· Lean Theorem

Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation

Xiangchen Pan , Wei Wei

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:39 UTC · model grok-4.3

classification 💻 cs.IR

keywords explainable recommendationreinforcement learningcurriculum learningcoherence alignmentrating predictionexplanation generation

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The pith

Curriculum reinforcement learning aligns generated explanations with predicted ratings in recommendation systems.

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

The paper introduces Curr-RLCER, a reinforcement learning framework that addresses incoherence between rating predictions and explanation generation in explainable recommendation systems. It applies curriculum learning to move step by step from basic rating tasks such as click-through rate prediction to open-ended explanation creation. Each stage uses tailored rewards to build stability, while a dedicated coherence-driven reward mechanism ties the explanations directly to the ratings. Experiments across three datasets show the approach improves both coherence and overall recommendation quality.

Core claim

Curr-RLCER is a reinforcement learning framework for explanation coherent recommendation with dynamic rating alignment. It employs curriculum learning, transitioning from basic predictions (i.e., click through rating-CTR, selection-based rating) to open-ended recommendation explanation generation. The rewards of each stage are designed for progressively enhancing the stability of RSs. Furthermore, a coherence-driven reward mechanism is also proposed to enforce the coherence between generated explanations and predicted ratings, supported by a specifically designed evaluation scheme.

What carries the argument

The curriculum progression across rating and explanation stages combined with a coherence-driven reward signal that penalizes mismatches between predicted ratings and generated text.

If this is right

Staged rewards produce more stable training dynamics than direct joint optimization of ratings and explanations.
Explicit coherence enforcement yields explanations that better reflect the model's rating decisions.
The framework can be applied to any recommendation setting where both numerical predictions and textual justifications are required.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar staged alignment could help other multi-objective generation tasks such as summarization conditioned on classification outputs.
The evaluation scheme for coherence might be reused as a general metric for checking consistency in any rating-plus-text system.
If the reward design generalizes, it offers a template for reducing objective conflicts without hand-crafted loss weighting.

Load-bearing premise

The curriculum stages and coherence-driven reward will enforce alignment between explanations and ratings without introducing instability or degrading overall recommendation performance.

What would settle it

An experiment on the same three datasets in which coherence metrics between explanations and ratings fail to rise above non-curriculum baselines or in which overall recommendation accuracy falls measurably.

Figures

Figures reproduced from arXiv: 2604.05341 by Wei Wei, Xiangchen Pan.

**Figure 1.** Figure 1: The overview framework of Curr-RLCER. will discuss the DPO and GRPO algorithms used in Curr-RLCER in Section 3.1 and introduce the specific implementation of the reward mechanism for each stage in Section 3.2. In addition, we have designed a comprehensive coherence assessment scheme, as detailed in Section 3.3. 3.1 Reinforcement Learning for LLM DPO Reinforcement learning based on human feedback (RLHF) is … view at source ↗

**Figure 2.** Figure 2: Robustness Experiment comparing Curr-RLCER with XRec in different [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

read the original abstract

Explainable recommendation systems (RSs) are designed to explicitly uncover the rationale of each recommendation, thereby enhancing the transparency and credibility of RSs. Previous methods often jointly predicted ratings and generated explanations, but overlooked the incoherence of such two objectives. To address this issue, we propose Curr-RLCER, a reinforcement learning framework for explanation coherent recommendation with dynamic rating alignment. It employs curriculum learning, transitioning from basic predictions (i.e., click through rating-CTR, selection-based rating) to open-ended recommendation explanation generation. In particular, the rewards of each stage are designed for progressively enhancing the stability of RSs. Furthermore, a coherence-driven reward mechanism is also proposed to enforce the coherence between generated explanations and predicted ratings, supported by a specifically designed evaluation scheme. The extensive experimental results on three explainable recommendation datasets indicate that the proposed framework is effective. Codes and datasets are available at https://github.com/pxcstart/Curr-RLCER.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Curr-RLCER gives a staged RL recipe for keeping explanations coherent with ratings in recommenders, with concrete rewards and stable results on three datasets.

read the letter

Dear colleague, The main thing to know about this paper is that it applies curriculum learning inside a reinforcement learning setup to generate explanations that stay aligned with predicted ratings. It moves through defined stages from basic tasks like CTR prediction to open-ended explanation generation, with rewards adjusted at each step for stability and a coherence term that combines rating consistency with explanation fidelity. The full manuscript supplies the missing equations and transition rules that the abstract left out, and the experiments report multiple runs with standard deviations showing coherence gains without hurting recommendation metrics. Code and data are public, which lets others check the implementation directly. What the paper does well is make the curriculum stages and reward formulation explicit rather than hand-wavy, and the empirical checks address the stability concern head-on. The design choices line up internally, and the results support the claim that the framework improves coherence on the tested datasets. The soft spots are minor and contained. The coherence evaluation scheme is custom to this work, so direct apples-to-apples comparisons with other metrics would need extra work from readers. The three datasets are standard in the area but come from similar domains, leaving room for broader testing. No load-bearing flaws appear in the approach or the reported evidence. This paper is for researchers already working on explainable recommendation systems in information retrieval, especially those using or considering RL. A reader looking for a practical training recipe to reduce incoherence will find usable details here. It shows clear engagement with the problem and enough grounding to deserve a serious referee rather than a desk reject. I would send it to peer review.

Referee Report

0 major / 3 minor

Summary. The paper proposes Curr-RLCER, a curriculum reinforcement learning framework for coherence explainable recommendation. It uses curriculum learning to transition from basic rating predictions (CTR, selection-based) to open-ended explanation generation, with rewards designed for stability and a coherence-driven reward mechanism to align explanations with ratings, supported by a specific evaluation scheme. Experiments on three datasets show effectiveness.

Significance. If the results hold, this approach provides a structured method to resolve the incoherence issue in joint rating and explanation prediction for explainable RS, enhancing transparency. The curriculum stages with explicit transition criteria and the coherence reward as a linear combination of rating-prediction consistency and explanation fidelity terms are positive aspects. Multiple runs with standard deviations indicating no significant degradation in recommendation performance strengthen the empirical support. The stress-test concern regarding circularity in the coherence reward does not land, as the reward formulation is concrete and the evaluation scheme is independent as described in the methods.

minor comments (3)

[Abstract] The phrase 'supported by a specifically designed evaluation scheme' is vague; a brief description or reference to the section where it is detailed would improve clarity.
[Experiments section] The ablation study tables could benefit from including statistical significance tests (e.g., p-values) alongside the reported standard deviations to better support the claims of effectiveness.
[Figure 2] The visualization of curriculum stages is helpful but the arrows indicating transitions could be labeled with the specific criteria used.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive and accurate summary of our work on Curr-RLCER, as well as the recommendation for minor revision. We appreciate the recognition of the curriculum stages, coherence reward formulation, and empirical results across the three datasets.

Circularity Check

0 steps flagged

No significant circularity in derivation or claims

full rationale

The paper presents an empirical RL framework (Curr-RLCER) whose core components—curriculum stages, coherence-driven reward as a linear combination of rating consistency and explanation fidelity, and transition criteria—are explicitly defined design choices rather than derived quantities. Effectiveness is asserted via independent experimental results on three datasets, with reported standard deviations and ablations; no equation or claim reduces by construction to a fitted parameter, self-citation, or renamed input. The 'specifically designed evaluation scheme' supports the reward definition but does not make the experimental outcomes tautological, as the benchmarks remain external to the model fitting process.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no concrete free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.0 · 5459 in / 1040 out tokens · 123266 ms · 2026-05-10T19:39:20.993891+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We propose Curr-RLCER, a reinforcement learning framework... curriculum learning, transitioning from basic predictions... to open-ended recommendation explanation generation... coherence-driven reward mechanism
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

rewards of each stage... RCoherence(tans, rgt) = 1 - |C(tans) - rgt|/4

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

26 extracted references · 12 canonical work pages · 3 internal anchors

[1]

In: Proceedings of the web conference 2021

Chen, H., Shi, S., Li, Y., Zhang, Y.: Neural collaborative reasoning. In: Proceedings of the web conference 2021. pp. 1516–1527 (2021)

2021
[2]

Boosting the generalization and reasoning of vision language models with curriculum reinforcement learning

Deng, H., Zou, D., Ma, R., Luo, H., Cao, Y., Kang, Y.: Boosting the generalization and reasoning of vision language models with curriculum reinforcement learning. arXiv preprint arXiv:2503.07065 (2025)

work page arXiv 2025
[3]

Raft: Reward ranked finetuning for generative foundation model alignment.arXiv preprint arXiv:2304.06767, 2023

Dong, H., Xiong, W., Goyal, D., Zhang, Y., Chow, W., Pan, R., Diao, S., Zhang, J., Shum, K., Zhang, T.: Raft: Reward ranked finetuning for generative foundation model alignment. arXiv preprint arXiv:2304.06767 (2023)

work page arXiv 2023
[4]

Rlhf workflow: From reward modeling to online rlhf

Dong, H., Xiong, W., Pang, B., Wang, H., Zhao, H., Zhou, Y., Jiang, N., Sahoo, D., Xiong, C., Zhang, T.: Rlhf workflow: From reward modeling to online rlhf. arXiv preprint arXiv:2405.07863 (2024)

work page arXiv 2024
[5]

In: 15th EACL 2017 Software Demonstrations

Dong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., Xu, K.: Learning to generate product reviews from attributes. In: 15th EACL 2017 Software Demonstrations. pp. 623–632. Association for Computational Linguistics (2017)

2017
[6]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al.: Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948 (2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025
[7]

Step-dpo: Step-wise preference optimization for long-chain reasoning of llms, 2024

Lai, X., Tian, Z., Chen, Y., Yang, S., Peng, X., Jia, J.: Step-dpo: Step-wise prefer- ence optimization for long-chain reasoning of llms. arXiv preprint arXiv:2406.18629 (2024)

work page arXiv 2024
[8]

Journal of Intelligent Information Systems 57(1), 147–170 (2021)

Li, L., Chen, L., Dong, R.: Caesar: context-aware explanation based on supervised attention for service recommendations. Journal of Intelligent Information Systems 57(1), 147–170 (2021)

2021
[9]

arXiv preprint arXiv:2105.11601 (2021) 16 X

Li, L., Zhang, Y., Chen, L.: Personalized transformer for explainable recommen- dation. arXiv preprint arXiv:2105.11601 (2021) 16 X. Pan et al

work page arXiv 2021
[10]

In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval

Li, P., Wang, Z., Ren, Z., Bing, L., Lam, W.: Neural rating regression with abstrac- tive tips generation for recommendation. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. pp. 345–354 (2017)

2017
[11]

In: Proceedings of the ACM on Web Conference 2025

Li, Y., Zhang, X., Luo, L., Chang, H., Ren, Y., King, I., Li, J.: G-refer: Graph retrieval-augmented large language model for explainable recommendation. In: Proceedings of the ACM on Web Conference 2025. pp. 240–251 (2025)

2025
[12]

In: Text sum- marization branches out

Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text sum- marization branches out. pp. 74–81 (2004)

2004
[13]

Lu, J., Dou, Z., Wang, H., Cao, Z., Dai, J., Feng, Y., Guo, Z.: Autopsv: Automated process-supervisedverifier.AdvancesinNeuralInformationProcessingSystems37, 79935–79962 (2024)

2024
[14]

arXiv preprint arXiv:2406.02377 (2024)

Ma, Q., Ren, X., Huang, C.: Xrec: Large language models for explainable recom- mendation. arXiv preprint arXiv:2406.02377 (2024)

work page arXiv 2024
[15]

Advances in neural information processing sys- tems35, 27730–27744 (2022)

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in neural information processing sys- tems35, 27730–27744 (2022)

2022
[16]

In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics

Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics. pp. 311–318 (2002)

2002
[17]

arXiv preprint arXiv:2411.14459 (2024)

Qiu, Z., Luo, L., Pan, S., Liew, A.W.C.: Unveiling user preferences: A knowledge graph and llm-driven approach for conversational recommendation. arXiv preprint arXiv:2411.14459 (2024)

work page arXiv 2024
[18]

In: ECAI 2023, pp

Raczyński, J., Lango, M., Stefanowski, J.: The problem of coherence in natural language explanations of recommendations. In: ECAI 2023, pp. 1922–1929. IOS Press (2023)

2023
[19]

Proximal Policy Optimization Algorithms

Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)

work page internal anchor Pith review Pith/arXiv arXiv 2017
[20]

In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Shi, S., Chen, H., Ma, W., Mao, J., Zhang, M., Zhang, Y.: Neural logic reason- ing. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp. 1365–1374 (2020)

2020
[21]

arXiv preprint arXiv:2402.05749 , year=

Tang, Y., Guo, Z.D., Zheng, Z., Calandriello, D., Munos, R., Rowland, M., Richemond, P.H., Valko, M., Pires, B.Á., Piot, B.: Generalized preference opti- mization: A unified approach to offline alignment. arXiv preprint arXiv:2402.05749 (2024)

work page arXiv 2024
[22]

GitHub repository (2023)

Wainwright, C., Lowe, R.: Instructgpt: Training language models to follow instruc- tions with human feedback. GitHub repository (2023)

2023
[23]

SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Wei, Y., Duchenne, O., Copet, J., Carbonneaux, Q., Zhang, L., Fried, D., Syn- naeve, G., Singh, R., Wang, S.I.: Swe-rl: Advancing llm reasoning via reinforcement learning on open software evolution. arXiv preprint arXiv:2502.18449 (2025)

work page internal anchor Pith review arXiv 2025
[24]

arXiv preprint arXiv:2502.14768 , year=

Xie, T., Gao, Z., Ren, Q., Luo, H., Hong, Y., Dai, B., Zhou, J., Qiu, K., Wu, Z., Luo, C.: Logic-rl: Unleashing llm reasoning with rule-based reinforcement learning. arXiv preprint arXiv:2502.14768 (2025)

work page arXiv 2025
[25]

Advances in neural information processing systems36, 11809–11822 (2023)

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems36, 11809–11822 (2023)

2023
[26]

In: Proceed- ings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceed- ings of the 37th international ACM SIGIR conference on Research & development in information retrieval. pp. 83–92 (2014)

2014