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GitHub 저장소

RewardHarness

RewardHarness에는 TIGER-AI-Lab에서 수집한 skills 76개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.

수집된 skills
76
Stars
14
업데이트
2026-05-16
Forks
1
직업 범위
직업 카테고리 15개 · 100% 분류됨
저장소 탐색

이 저장소의 skills

instruction-following-completeness
데이터 과학자

Score completeness of instruction execution. Partial edits get partial credit; missed sub-steps cost points.

2026-05-16
realism-and-artifact-penalties
데이터 과학자

Guidance on penalizing artifacts while allowing conceptual unrealism if requested by the prompt.

2026-05-16
text-and-ocr-analyzer
데이터 과학자

Extracts and analyzes text within images to verify spelling, placement, and clarity.

2026-05-16
evaluating-background-changes
데이터 과학자

Guidelines for evaluating background replacements and scene changes.

2026-05-14
evaluating-multi-step-edits
데이터 과학자

Guidelines for evaluating prompts with multiple distinct instructions.

2026-05-14
evaluating-object-replacements
도매·소매 바이어(농산물 제외)

Guidelines for evaluating object replacements and modifications.

2026-05-14
evaluating-style-transfers
데이터 과학자

Guidelines for evaluating artistic style transfers.

2026-05-14
evaluating-text-rendering
그래픽 디자이너

Guidelines for evaluating text additions and modifications in images.

2026-05-14
compare-images
데이터 과학자

Compare a source image and edited images to identify differences, check if instructions were followed, and assess visual quality.

2026-05-14
artifact-and-structure-preservation
데이터 과학자

Rules for prioritizing image integrity and penalizing severe artifacts or loss of original structure.

2026-05-14
artistic-and-cultural-references
아트 디렉터

Guidelines for evaluating prompts that reference specific artists, art styles, or cultural phenomena.

2026-05-14
embrace-intended-drastic-changes
데이터 과학자

Rules for evaluating setting and style changes, strictly forbidding penalizing an image for losing original context when a new setting is requested.

2026-05-14
enforce-logical-consistency
데이터 과학자

Ensures the reasoning chain does not contain internal contradictions between stated facts and image evaluations.

2026-05-14
entity-definition-activation
데이터 과학자

Requires explicitly defining specific artists, styles, or entities and their visual hallmarks before evaluating.

2026-05-14
environmental-blending-and-lighting-adaptation
데이터 과학자

Rules for evaluating background changes, rewarding necessary adaptations to the subject's lighting, shadows, and ground contact.

2026-05-14
evaluate-insertions-and-anatomical-integrity
특수 효과 아티스트 및 애니메이터

Guidelines for evaluating inserted humans, animals, or objects, prioritizing anatomical correctness over superficial integration.

2026-05-14
evaluate-physical-plausibility
데이터 과학자

Guidelines for evaluating inserted or replaced objects for physical plausibility, penalizing floating objects or gravity-defying placements.

2026-05-14
evaluate-text-additions
데이터 과학자

Guidelines for evaluating text additions, strictly prioritizing exact string matches and legibility over natural integration.

2026-05-14
holistic-evaluation-vs-tunnel-vision
소프트웨어 품질 보증 분석가·테스터

Prevents tunnel vision on specific requested edits by enforcing holistic evaluation, penalizing collateral damage, anatomical distortion, and loss of original context.

2026-05-14
instruction-over-naturalness
데이터 과학자

Prioritizes instruction fulfillment over naturalness, warning against the 'subtlety trap' and 'preservation bias'.

2026-05-14
mitigate-positional-bias
데이터 과학자

Strategies to prevent defaulting to Image B and ensure fair evaluation of both images.

2026-05-14
object-replacement-proportions-and-exaggeration
웹·디지털 인터페이스 디자이너

Guidelines for evaluating object replacements where the new object is inherently bulkier or different in proportion (e.g., snow goggles vs glasses).

2026-05-14
overcoming-default-to-a-bias
데이터 과학자

Strategies to combat the strong bias of defaulting to Image A by hallucinating details or overvaluing naturalness.

2026-05-14
penalize-gibberish-text
데이터 과학자

Guidelines for penalizing gibberish text, establishing that instruction fulfillment strictly outweighs text artifacts.

2026-05-14
penalize-unintended-style-changes
아트 디렉터

Guidelines for penalizing images that alter the original art style when not requested, with exceptions for intended drastic changes.

2026-05-14
preserve-subject-interaction-and-pose
기타 심리학자

Guidelines for preserving realistic interaction and strict anatomical integrity when an object is replaced.

2026-05-14
prevent-image-feature-swapping
데이터 과학자

Mandates strict verification of which image contains which features to prevent attributing Image A's details to Image B.

2026-05-14
reasoning-preference-alignment
데이터 과학자

Ensures the final preference logically matches the comparative analysis in the reasoning chain.

2026-05-14
strict-semantic-and-conceptual-alignment
데이터 과학자

Ensures strict adherence to requested concepts, rejecting semantic near-misses (e.g., classroom vs cafeteria).

2026-05-14
subject-vs-setting-modifications
특수 효과 아티스트 및 애니메이터영화·비디오 편집자

Guidelines for preserving the main subject during setting changes, penalizing subject destruction.

2026-05-14
verify-extreme-claims-and-hallucinations
데이터 과학자

Guidelines to prevent hallucinating extreme failures (e.g., 'blank black screen') and ensure logical consistency.

2026-05-14
verify-instruction-target-and-spatial-relations
소프트웨어 품질 보증 분석가·테스터

Ensures edits are applied to the exact target, strictly enforcing spatial prepositions (under, behind) and prioritizing correct placement over natural integration.

2026-05-14
object-locator-and-counter
데이터 과학자

Detects, counts, and locates specific objects in the image, useful for instructions targeting a specific instance (e.g., 'the 4th surfboard').

2026-05-14
side-by-side-image-comparator
데이터 과학자

Compares two images side-by-side to definitively determine which image contains specific objects, text, or features, preventing feature-swapping.

2026-05-14
text-and-spelling-checker
편집자

Extracts and verifies text in the image, checking for exact string matches, spelling errors, and legibility.

2026-05-14
visual-detail-and-setting-analyzer
데이터 과학자

Analyzes images to verify exact settings (e.g., cafeteria vs classroom), fine-grained details, and extreme artifacts.

2026-05-14
visual-question-answering
데이터 과학자

Answers specific visual questions about the images to verify the presence of objects, text, spatial relations, or specific attributes.

2026-05-14
evaluating-multiple-constraints
데이터 과학자

How to systematically evaluate prompts with more than one instruction.

2026-05-14
evaluating-style-transfers
데이터 과학자

Guidelines for evaluating artistic style transfers and thematic changes.

2026-05-14
evaluating-text-additions
데이터 과학자

Guidelines for evaluating the addition or modification of text in images.

2026-05-14
이 저장소에서 수집된 skills 76개 중 상위 40개를 표시합니다.