Structured PREreview of "RubricRL: Simple Generalizable Rewards for Text-to-Image Generation" Article Swipe
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/17731002. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Highly appropriate Are the conclusions supported by the data? Highly supported Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly Is the preprint likely to advance academic knowledge? Highly likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, it's of high quality Is it ready for attention from an editor, publisher or broader audience? Yes, as it is Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Related Topics
Concepts
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17731002
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7107859685
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7107859685Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.17731002Digital Object Identifier
- Title
-
Structured PREreview of "RubricRL: Simple Generalizable Rewards for Text-to-Image Generation"Work title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-27Full publication date if available
- Authors
-
Akshaan BandaraList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17731002Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17731002Direct OA link when available
- Concepts
-
Preprint, Computer science, Quality (philosophy), Simple (philosophy), Artificial intelligence, Generative grammar, Data science, Epistemology, Psychology, Key (lock), Point (geometry), Management science, Acronym, Machine learning, Scheme (mathematics), Perspective (graphical)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W7107859685 |
|---|---|
| doi | https://doi.org/10.5281/zenodo.17731002 |
| ids.openalex | https://openalex.org/W7107859685 |
| fwci | 0.0 |
| type | review |
| title | Structured PREreview of "RubricRL: Simple Generalizable Rewards for Text-to-Image Generation" |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C43169469 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7410255670547485 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q580922 |
| concepts[0].display_name | Preprint |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6111395955085754 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2779530757 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5499410033226013 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1207505 |
| concepts[2].display_name | Quality (philosophy) |
| concepts[3].id | https://openalex.org/C2780586882 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5363740921020508 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7520643 |
| concepts[3].display_name | Simple (philosophy) |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.48813527822494507 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C39890363 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4389653205871582 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q36108 |
| concepts[5].display_name | Generative grammar |
| concepts[6].id | https://openalex.org/C2522767166 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3622679114341736 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[6].display_name | Data science |
| concepts[7].id | https://openalex.org/C111472728 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3164881467819214 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[7].display_name | Epistemology |
| concepts[8].id | https://openalex.org/C15744967 |
| concepts[8].level | 0 |
| concepts[8].score | 0.31591805815696716 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[8].display_name | Psychology |
| concepts[9].id | https://openalex.org/C26517878 |
| concepts[9].level | 2 |
| concepts[9].score | 0.29170161485671997 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[9].display_name | Key (lock) |
| concepts[10].id | https://openalex.org/C28719098 |
| concepts[10].level | 2 |
| concepts[10].score | 0.28417137265205383 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[10].display_name | Point (geometry) |
| concepts[11].id | https://openalex.org/C539667460 |
| concepts[11].level | 1 |
| concepts[11].score | 0.2706919312477112 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2414942 |
| concepts[11].display_name | Management science |
| concepts[12].id | https://openalex.org/C482391 |
| concepts[12].level | 2 |
| concepts[12].score | 0.26591628789901733 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q101244 |
| concepts[12].display_name | Acronym |
| concepts[13].id | https://openalex.org/C119857082 |
| concepts[13].level | 1 |
| concepts[13].score | 0.2638005018234253 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[13].display_name | Machine learning |
| concepts[14].id | https://openalex.org/C77618280 |
| concepts[14].level | 2 |
| concepts[14].score | 0.2621743977069855 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1155772 |
| concepts[14].display_name | Scheme (mathematics) |
| concepts[15].id | https://openalex.org/C12713177 |
| concepts[15].level | 2 |
| concepts[15].score | 0.2512050271034241 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q1900281 |
| concepts[15].display_name | Perspective (graphical) |
| keywords[0].id | https://openalex.org/keywords/preprint |
| keywords[0].score | 0.7410255670547485 |
| keywords[0].display_name | Preprint |
| keywords[1].id | https://openalex.org/keywords/quality |
| keywords[1].score | 0.5499410033226013 |
| keywords[1].display_name | Quality (philosophy) |
| keywords[2].id | https://openalex.org/keywords/simple |
| keywords[2].score | 0.5363740921020508 |
| keywords[2].display_name | Simple (philosophy) |
| keywords[3].id | https://openalex.org/keywords/generative-grammar |
| keywords[3].score | 0.4389653205871582 |
| keywords[3].display_name | Generative grammar |
| keywords[4].id | https://openalex.org/keywords/key |
| keywords[4].score | 0.29170161485671997 |
| keywords[4].display_name | Key (lock) |
| keywords[5].id | https://openalex.org/keywords/point |
| keywords[5].score | 0.28417137265205383 |
| keywords[5].display_name | Point (geometry) |
| language | en |
| locations[0].id | pmh:oai:zenodo.org:17731002 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400562 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[0].source.host_organization | https://openalex.org/I67311998 |
| locations[0].source.host_organization_name | European Organization for Nuclear Research |
| locations[0].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | info:eu-repo/semantics/review |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5281/zenodo.17731002 |
| authorships[0].author.id | |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Akshaan Bandara |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Akshaan Bandara |
| authorships[0].is_corresponding | True |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5281/zenodo.17731002 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-11-28T00:00:00 |
| display_name | Structured PREreview of "RubricRL: Simple Generalizable Rewards for Text-to-Image Generation" |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-12-01T00:07:19.613710 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:zenodo.org:17731002 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400562 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| best_oa_location.source.host_organization | https://openalex.org/I67311998 |
| best_oa_location.source.host_organization_name | European Organization for Nuclear Research |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | info:eu-repo/semantics/review |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5281/zenodo.17731002 |
| primary_location.id | pmh:oai:zenodo.org:17731002 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400562 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| primary_location.source.host_organization | https://openalex.org/I67311998 |
| primary_location.source.host_organization_name | European Organization for Nuclear Research |
| primary_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | info:eu-repo/semantics/review |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5281/zenodo.17731002 |
| publication_date | 2025-11-27 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 4, 9 |
| abstract_inverted_index.AI | 176 |
| abstract_inverted_index.Is | 99, 134 |
| abstract_inverted_index.No | 117 |
| abstract_inverted_index.an | 140 |
| abstract_inverted_index.as | 147 |
| abstract_inverted_index.at | 18 |
| abstract_inverted_index.by | 53 |
| abstract_inverted_index.do | 79 |
| abstract_inverted_index.in | 32 |
| abstract_inverted_index.is | 3, 149 |
| abstract_inverted_index.it | 112, 135, 148 |
| abstract_inverted_index.no | 159 |
| abstract_inverted_index.of | 8, 28, 129, 163 |
| abstract_inverted_index.or | 143 |
| abstract_inverted_index.to | 67, 103, 125, 177 |
| abstract_inverted_index.up | 179 |
| abstract_inverted_index.Are | 38, 49, 60 |
| abstract_inverted_index.How | 77 |
| abstract_inverted_index.Yes | 35 |
| abstract_inverted_index.You | 12 |
| abstract_inverted_index.and | 73, 84, 88 |
| abstract_inverted_index.can | 13 |
| abstract_inverted_index.did | 172 |
| abstract_inverted_index.for | 42, 92, 137, 183 |
| abstract_inverted_index.new | 181 |
| abstract_inverted_index.not | 173 |
| abstract_inverted_index.the | 15, 23, 26, 29, 33, 39, 50, 54, 61, 69, 80, 93, 100 |
| abstract_inverted_index.use | 174 |
| abstract_inverted_index.you | 121 |
| abstract_inverted_index.Does | 22 |
| abstract_inverted_index.Very | 95 |
| abstract_inverted_index.Yes, | 127, 146 |
| abstract_inverted_index.come | 178 |
| abstract_inverted_index.data | 62 |
| abstract_inverted_index.from | 114, 139 |
| abstract_inverted_index.have | 158 |
| abstract_inverted_index.high | 130 |
| abstract_inverted_index.it's | 128 |
| abstract_inverted_index.next | 90 |
| abstract_inverted_index.that | 156, 170 |
| abstract_inverted_index.they | 157, 171 |
| abstract_inverted_index.this | 43, 123 |
| abstract_inverted_index.view | 14 |
| abstract_inverted_index.with | 180 |
| abstract_inverted_index.<a | 19 |
| abstract_inverted_index.Would | 111, 120 |
| abstract_inverted_index.clear | 74 |
| abstract_inverted_index.data? | 55, 70 |
| abstract_inverted_index.ideas | 182 |
| abstract_inverted_index.ready | 136 |
| abstract_inverted_index.steps | 91 |
| abstract_inverted_index.their | 86, 184 |
| abstract_inverted_index.Highly | 45, 56, 71, 107 |
| abstract_inverted_index.Zenodo | 1 |
| abstract_inverted_index.author | 154, 168 |
| abstract_inverted_index.likely | 102, 108 |
| abstract_inverted_index.record | 2 |
| abstract_inverted_index.advance | 104 |
| abstract_inverted_index.authors | 81 |
| abstract_inverted_index.benefit | 113 |
| abstract_inverted_index.broader | 144 |
| abstract_inverted_index.clearly | 78, 96 |
| abstract_inverted_index.editor, | 141 |
| abstract_inverted_index.explain | 25 |
| abstract_inverted_index.methods | 40 |
| abstract_inverted_index.others? | 126 |
| abstract_inverted_index.quality | 131 |
| abstract_inverted_index.version | 7 |
| abstract_inverted_index.academic | 105 |
| abstract_inverted_index.complete | 16 |
| abstract_inverted_index.declares | 155, 169 |
| abstract_inverted_index.discuss, | 82 |
| abstract_inverted_index.editing? | 116 |
| abstract_inverted_index.explain, | 83 |
| abstract_inverted_index.findings | 87 |
| abstract_inverted_index.language | 115 |
| abstract_inverted_index.preprint | 101, 124 |
| abstract_inverted_index.research | 30 |
| abstract_inverted_index.PREreview | 17 |
| abstract_inverted_index.attention | 138 |
| abstract_inverted_index.audience? | 145 |
| abstract_inverted_index.competing | 160 |
| abstract_inverted_index.including | 64 |
| abstract_inverted_index.interpret | 85 |
| abstract_inverted_index.objective | 27 |
| abstract_inverted_index.potential | 89 |
| abstract_inverted_index.preprint? | 34 |
| abstract_inverted_index.presented | 31 |
| abstract_inverted_index.preserved | 6 |
| abstract_inverted_index.publisher | 142 |
| abstract_inverted_index.recommend | 122 |
| abstract_inverted_index.represent | 68 |
| abstract_inverted_index.research? | 44, 94 |
| abstract_inverted_index.supported | 52, 57 |
| abstract_inverted_index.Artificial | 164 |
| abstract_inverted_index.PREreview. | 11 |
| abstract_inverted_index.Structured | 10 |
| abstract_inverted_index.generative | 175 |
| abstract_inverted_index.knowledge? | 106 |
| abstract_inverted_index.<div> | 21, 37, 48, 59, 76, 98, 110, 119, 133 |
| abstract_inverted_index.appropriate | 46, 72 |
| abstract_inverted_index.conclusions | 51 |
| abstract_inverted_index.permanently | 5 |
| abstract_inverted_index.well-suited | 41, 66 |
| abstract_inverted_index.</div> | 36, 47, 58, 75, 97, 109, 118, 132, 150 |
| abstract_inverted_index.<p>The | 153, 167 |
| abstract_inverted_index.Intelligence | 165 |
| abstract_inverted_index.introduction | 24 |
| abstract_inverted_index.<h2>Use | 162 |
| abstract_inverted_index.presentations, | 63 |
| abstract_inverted_index.(AI)</h2> | 166 |
| abstract_inverted_index.visualizations, | 65 |
| abstract_inverted_index.review.</p> | 185 |
| abstract_inverted_index.<h2>Competing | 151 |
| abstract_inverted_index.interests</h2> | 152 |
| abstract_inverted_index.interests.</p> | 161 |
| abstract_inverted_index.<p><strong>This | 0 |
| abstract_inverted_index.href="https://prereview.org/reviews/17731002">https://prereview.org/reviews/17731002</a>.</strong></p> | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |