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arxiv: 2605.18261 · v1 · pith:LEGCNI67new · submitted 2026-05-18 · 💻 cs.CL

Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains

Zhonghang Yuan , Zhefan Wang , Fang Hu , Zihong Chen , Jinzhe Li , Gang Li , Jie Ying , Huanjun Kong
show 2 more authors
Songyang Zhang Nanqing Dong
This is my paper

Pith reviewed 2026-05-20 10:16 UTC · model grok-4.3

classification 💻 cs.CL
keywords RLVRlarge language modelsknowledge-intensive domainsautomated data synthesisreasoning verificationreinforcement learningLLM reasoning
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The pith

K2V extends RLVR to knowledge-intensive domains by automating verifiable reasoning data synthesis and checking the full process rather than final answers alone.

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

The paper sets out to demonstrate that reinforcement learning with verifiable rewards can move beyond math and coding into areas that demand broad factual and domain knowledge. It does so by creating an automated pipeline that turns raw knowledge into chains of verifiable steps and then rewards models for getting both the steps and the outcome right. Experiments indicate these changes lift performance on knowledge tasks while leaving general abilities mostly unchanged. A reader would care because the approach replaces scarce manual data with scalable synthesis and replaces sparse end-of-answer rewards with denser process signals.

Core claim

We introduce the Knowledge-to-Verification (K2V) framework that extends RLVR to knowledge-intensive domains through automated verifiable data synthesis while enabling verification of the LLM's reasoning process. This addresses data scarcity and the limitations of final-answer-only rewards that produce flawed intermediate reasoning and sparse training signals. Extensive experiments show that K2V improves reasoning performance in knowledge-intensive domains without significantly compromising the model's general capabilities, indicating that automated synthesis paired with reasoning verification offers a viable path for broader domains.

What carries the argument

The K2V framework, which performs automated synthesis of verifiable reasoning chains and incorporates step-by-step process verification into the RLVR reward model.

If this is right

  • LLMs produce more reliable step-by-step reasoning on factual and domain-specific tasks.
  • Training no longer depends primarily on manually created verifiable datasets.
  • Sparse final-answer rewards are replaced by denser process-level signals.
  • The same synthesis-plus-verification pattern can be reused across additional knowledge-heavy applications.

Where Pith is reading between the lines

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

  • The synthesis technique may transfer to other training methods such as supervised fine-tuning or preference optimization.
  • Domains like scientific literature or regulatory compliance could benefit from similar automated verification pipelines.
  • Combining K2V with external knowledge retrieval systems could further strengthen the quality of the synthesized chains.

Load-bearing premise

The automated synthesis process produces reasoning chains that are high-quality, verifiable, and representative of real knowledge-intensive domain demands rather than introducing artifacts or oversimplifications.

What would settle it

If K2V-trained models show no gains on knowledge-intensive benchmarks or clear drops on general capability tests relative to standard RLVR or supervised baselines, the central claim would not hold.

Figures

Figures reproduced from arXiv: 2605.18261 by Fang Hu, Gang Li, Huanjun Kong, Jie Ying, Jinzhe Li, Nanqing Dong, Songyang Zhang, Zhefan Wang, Zhonghang Yuan, Zihong Chen.

Figure 1
Figure 1. Figure 1: An overview of K2V. (a) K2V begins by constructing a KG from unstructured corpora. It then samples quintuples from the KG and randomly masks an entity. This masked quintuple is then converted into a fill-blank style question, where the name of the masked entity serves as the verifiable ground truth. (b) Given a QA pair, the Policy Model generates a reasoning process. To verify this reasoning process, K2V f… view at source ↗
Figure 2
Figure 2. Figure 2: The accuracy of K2V-7B-Qwen and Qwen2.5- [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training dynamics of ablation studies. The [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The first case of K2V [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The second case of K2V [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The third case of K2V [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Three cases of Liquid. The data synthesized by Liquid contains multiple candidate answers, which are [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Three cases of Genie [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Three cases of SDR [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Three cases of BDS [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗
read the original abstract

Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs) in domains such as mathematics and coding. However, its applications on knowledge-intensive domains have not been effectively explored due to the scarcity of high-quality verifiable data. Furthermore, current RLVR focuses solely on the correctness of final answers, leading to the limitations of flawed reasoning and sparse reward signals. In this work, we propose Knowledge-to-Verification (K2V), a framework that extends RLVR to knowledge-intensive domains through automated verifiable data synthesis, while enabling verification of the LLM's reasoning process. Extensive experiments demonstrate that K2V enhances the reasoning of LLM in knowledge-intensive domains without significantly compromising the model's general capabilities. This study also suggests that integrating automated data synthesis with reasoning verification is a promising direction to enhance model capabilities in these broader domains. Code is available at https://github.com/SeedScientist/K2V.

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.

Referee Report

2 major / 2 minor

Summary. The paper introduces Knowledge-to-Verification (K2V), a framework extending RLVR to knowledge-intensive domains via automated synthesis of verifiable reasoning chains. This enables process-level verification beyond final-answer correctness. The central empirical claim is that K2V improves LLM reasoning in knowledge-intensive domains while preserving general capabilities, supported by extensive experiments and an open-source implementation.

Significance. If the synthesis pipeline produces reasoning chains whose verification signals and complexity genuinely match authentic knowledge-intensive tasks, the work would meaningfully broaden RLVR applicability beyond mathematics and coding. The process-verification component addresses a known limitation of outcome-only rewards and the reproducible code release strengthens the contribution.

major comments (2)
  1. [§3] §3 (Automated Synthesis): the description of the data-generation pipeline does not specify whether external grounding (e.g., retrieval from knowledge bases) or LLM self-generation is used; without this, it is impossible to evaluate the skeptic concern that the resulting chains may be artifactually simplified or easier to verify than real domain demands.
  2. [§4] §4 (Experiments): the reported gains lack accompanying verification accuracy metrics for the synthesized chains, baseline implementation details, and statistical controls (e.g., multiple seeds, effect sizes, or significance tests) for the claim that general capabilities are not compromised; these omissions make it difficult to rule out post-hoc selection or distribution shift artifacts.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'extensive experiments' is used without any quantitative summary or domain list, reducing informativeness for readers.
  2. [§2] Notation: the distinction between 'verifiable reasoning chains' and standard chain-of-thought is introduced but not formalized with a precise definition or example in the early sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and detailed comments on our manuscript. We address each major comment below in a point-by-point manner. Revisions have been made to clarify the synthesis pipeline and to strengthen the experimental reporting with additional metrics and controls.

read point-by-point responses

  1. Referee: [§3] §3 (Automated Synthesis): the description of the data-generation pipeline does not specify whether external grounding (e.g., retrieval from knowledge bases) or LLM self-generation is used; without this, it is impossible to evaluate the skeptic concern that the resulting chains may be artifactually simplified or easier to verify than real domain demands.

    Authors: We appreciate the referee's observation regarding the lack of explicit detail on the grounding mechanism. The synthesis pipeline in the original manuscript is based on LLM self-generation using domain-specific prompts derived from the source knowledge, combined with iterative self-verification steps to produce reasoning chains. No external knowledge base retrieval is employed. We have revised Section 3 to state this explicitly and added a discussion paragraph acknowledging the risk of artifactual simplification relative to authentic domain tasks, along with future directions for retrieval-augmented variants. This clarification should allow readers to better assess the skeptic concern. revision: yes

  2. Referee: [§4] §4 (Experiments): the reported gains lack accompanying verification accuracy metrics for the synthesized chains, baseline implementation details, and statistical controls (e.g., multiple seeds, effect sizes, or significance tests) for the claim that general capabilities are not compromised; these omissions make it difficult to rule out post-hoc selection or distribution shift artifacts.

    Authors: We agree that these details were insufficient in the original submission. In the revised manuscript, we now report verification accuracy metrics for the synthesized chains (measured via independent LLM judges and human spot-checks on a subset), provide fuller baseline implementation details (including hyperparameters and prompt templates), and include statistical controls: results are averaged over 5 random seeds, with effect sizes (Cohen's d) and significance tests (paired t-tests with p-values) for both domain-specific gains and preservation of general capabilities. These additions reduce the possibility of post-hoc selection or shift artifacts. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on new synthesis and experiments

full rationale

The paper introduces the K2V framework for RLVR in knowledge-intensive domains through automated verifiable data synthesis and process verification. Its strongest claims are backed by extensive experiments demonstrating improved reasoning without compromising general capabilities. No load-bearing steps reduce by construction to fitted inputs, self-definitions, or self-citation chains; the central results derive from external evaluation on synthesized data rather than renaming or re-deriving prior outputs. The work is self-contained against benchmarks, with the synthesis step presented as a methodological contribution rather than a tautological fit.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework depends on the assumption that automated synthesis can produce reasoning chains whose correctness can be verified without human labels; no explicit free parameters or invented physical entities are mentioned.

axioms (1)
  • domain assumption Automated synthesis can generate questions and reasoning chains whose correctness is reliably checkable by rule-based or model-based verifiers.
    This premise is required for the RLVR extension to work in knowledge domains and is invoked when the abstract describes 'automated verifiable data synthesis'.

pith-pipeline@v0.9.0 · 5724 in / 1255 out tokens · 35545 ms · 2026-05-20T10:16:03.171258+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

100 extracted references · 100 canonical work pages · 4 internal anchors

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    To ensure a fair comparison with our K2V method, we employ Qwen2.5-72B-Instruct for the generation stage

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    yes” or “no

    as a baseline, which consists of three stages: (1) Content Preparation: We construct a knowl- edge graph and systematically extract 4-node sim- ple paths (3-hop relations) as logical chains for question generation. To ensure computational fea- sibility, we limit the maximum number of paths to 20,000 and restrict source nodes to those with outgoing edges, ...

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    Correctly states that the TRPV4 gene is located on chromosome 12q23-24

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    Describes the role of the TRPV4 protein in calcium signaling and mechanosensation

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    States that mutations in the TRPV4 gene lead to CMT2C and other neurological and musculoskeletal disorders

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    Explains that the dysfunction of the TRPV4 protein due to genetic mutations is a key factor in the development of CMT2C and its related symptoms

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    Figure 4: The first case of K2V

    he overall response is well-structured, logically coherent, and clearly written, avoiding self-contradictions and redundant statements. Figure 4: The first case of K2V . Question: { } is a critical aspect of gene expression regulation that occurs after the RNA has been transcribed. This process encompasses several key steps, including mRNA processing, spl...

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    Clearly describes the key steps involved in post-transcriptional control, including mRNA processing, splicing, export, turnover, and translational control

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    Correctly explains mRNA turnover as a crucial component of post-transcriptional control, involving the degradation of mRNA molecules to regulate the amount of mRNA available for translation

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    Accurately describes translational control as another essential process in post- transcriptional control, involving the regulation of mRNA translation into proteins to ensure the correct amount and type of proteins are produced

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    Explains the role of mRNA-binding proteins in post-transcriptional control, including their function in regulating mRNA stability, transport, and translation

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    Uses appropriate biological terminology and concepts to describe the processes involved in post-transcriptional control

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    Figure 5: The second case of K2V

    Avoids over-extrapolation or unfounded speculation beyond the scope of the given information. Figure 5: The second case of K2V . Question: The { }, located in the lower medulla oblongata, plays a crucial role in processing sensory information from the lower half of the body, particularly for touch and proprioception. This nucleus receives input from large...

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    Correctly identifies the gracile nucleus as the structure located in the lower medulla oblongata

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    Clearly explains that once these fibers reach the gracile nucleus, they synapse, and the axons of the second-order neurons arise from this nucleus

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    Correctly describes the decussation (crossing over) of the axons of the second-order neurons to the contralateral side

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    Accurately states that the axons of the second-order neurons continue their journey to the thalamus, forming part of the lemniscal pathway

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    Figure 6: The third case of K2V

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    Following a clear logical flow and structure

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    Establishing proper cause-and-effect relationships

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    Ensuring temporal and sequential consistency

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    —Instructions—

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    This prompt is used to instructs an LLM to convert the masked quintuple into a fill-blank style QA pair

    Review and refine the text to ensure: - Logical consistency throughout - Clear cause-and-effect relationships ################ -ENTITIES- ################ {entities} ################ -RELATIONSHIPS- ################ {relationships} Table 13: Prompt of the Ftext. This prompt is used to instructs an LLM to convert the masked quintuple into a fill-blank styl...

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    relationship

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    content_keywords

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    Use **##** as the list delimiter

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    { }" indicating the content to be filled in. A fill-in-the-blank question may contain multiple

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    Statistics and Evaluation:

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    criteria 1

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    Accurately defines the core biological concepts involved in the question

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    Accurately explains the meaning and relationships represented by abstract biological models in words

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    Applies abstract biological concepts to the given specific scenario

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    Scientific Method and Design:

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    Predicts the likely consequences of a change (e.g., disturbance, mutation) to a system based on biological principles

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    Explains the underlying biological reason for an observed phenomenon or experimental result

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    Table 17: General criteria in the agricultural domain

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    Accurately defines the core medical concepts involved in the question

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    Clearly describes the involved medical processes in the correct logical sequence

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    Accurately explains the meaning and relationships represented by abstract biological or medical models in words

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    Applies abstract biological or medical concepts to the given specific scenario

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  60. [91]

    Scientific Method and Design:

    Correctly explains the connection between a medical concept or process and other related principles. Scientific Method and Design:

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    Clearly states a relevant null hypothesis or alternative hypothesis

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    Accurately identifies the independent, dependent, and key control variables of an experiment

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    Makes a logical and reasonable prediction of the experimental outcome based on a scientific hypothesis

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    Data Processing and Analysis:

    Evaluates the validity or potential flaws of a given experimental design. Data Processing and Analysis:

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    Accurately and correctly extracts key data points

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    Clearly and comprehensively describes the overall trend or significant patterns in the given data

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    Accurately describes the relationship between variables (e.g., positive correlation, negative correlation, no correlation)

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  68. [99]

    Statistics and Evaluation:

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  69. [100]

    In appropriate contexts, correctly uses statistical concepts to explain the reliability of data

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  70. [101]

    support,

    Based on data analysis, draws a conclusion of "support," "refute," or "inconclusive" for a given scientific hypothesis

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  71. [102]

    Argumentation and Reasoning:

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  72. [103]

    Makes a scientific claim that is specific and supported by concrete evidence

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    Clearly articulates how the evidence supports the scientific claim, demonstrating a strong logical chain

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    Predicts the likely consequences of a change (e.g., disturbance, mutation) to a system based on biological or medical principles

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    Explains the underlying biological or medical reason for an observed phenomenon or experimental result

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  76. [107]

    Avoids over-extrapolation or unfounded speculation beyond the scope of the given evidence

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  77. [108]

    Based on diagnostic or analytical results, proposes specific and feasible treatment or management recommenda- tions that comply with clinical guidelines and ethical principles

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  78. [109]

    Clearly articulates the rationale for the proposed recommendations and weighs their potential benefits and risks

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  79. [110]

    Be able to ignore irrelevant information and focus on answering the question directly

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  80. [111]

    Table 18: General criteria in the medical domain General criteria in the legal domain Fact and Issue Identification:

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