Translate Hugging Face blog post: Training a coding model to paint watercolours with TRL and OpenEnv

[Jwaminju]

Source: https://huggingface.co/blog/train-to-paint-with-code

This PR adds a Korean translation draft for train-to-paint-with-code.

Downstream handoff:

• SEO review should use the translation-flow manifest.
• Quality review should use the translation-flow manifest.

[github-actions]

PR Preview Action v1.8.1
🚀 View preview at https://hugging-face-krew.github.io/pr-preview/pr-204/
Built to branch gh-pages at 2026-09-04 04:27 UTC. Preview will be ready when the GitHub Pages deployment is complete.

[Jwaminju]

HF Agent Review

Gate	Result
Quality	❌ Fail
SEO	✅ Pass

Head SHA: 5d60065928ac1f101cdeb33597aa59829aed1bab

Quality report — ❌ Fail

Quality Report

• Status: reject
• Quality Score: 0.0
• Hard failures: 100
• Issues: 389
• Source available: True
• Source changed: False
• Source segments: 150
• Target segments: 141

Scorecard

Dimension	Score
adequacy	0.0
technical_accuracy	0.0
completeness	0.0
terminology	0.0
fluency	0.0
publishing_integrity	0.0
style_locale	60.0

Metrics

• qe_metric: heuristic
• qe_average: 0.6825
• qe_min: 0.0
• embedding_similarity_average: 0.6035
• embedding_similarity_min: 0.0314
• cache_hits: 0
• cache_misses: 282

MQM Judge

• Enabled: True
• Provider: openai
• Model: gpt-5.6-luna
• Reasoning effort: none
• Prompt: /home/runner/work/hugging-face-krew.github.io/hugging-face-krew.github.io/workflow/skills/quality/judges/mqm_prompt.md
• Prompt hash: 887d2931aa289213f0bdce4a917a0ac8364dad8e470011069b8f8bf758d69a90
• Style guide hash: 937d8cd893578d30e716a3eb513cdf5f10d6fd3ad8f5e77068b57f96e160de12
• Requested segments: 141
• Evaluated segments: 137
• MQM errors: 158
• Cache hits: 0
• Cache misses: 141
• Severity counts: {'critical': 99, 'major': 18, 'minor': 41}
• adequacy_average: 0.3223
• technical_average: 0.5914
• fluency_average: 0.7983
• warning: Skipped MQM error for segment b_044: target_span is not verbatim target text.
• warning: Skipped MQM result for segment b_044: at least one error was invalid.
• warning: Skipped MQM error for segment p_046: source_span is not verbatim source text.
• warning: Skipped MQM result for segment p_046: at least one error was invalid.
• warning: Skipped MQM error for segment p_047: source_span is not verbatim source text.
• warning: Skipped MQM result for segment p_047: at least one error was invalid.
• warning: Skipped MQM error for segment p_092: target_span is not verbatim target text.
• warning: Skipped MQM result for segment p_092: at least one error was invalid.
• warning: MQM segment coverage is invalid: expected exactly one result for every aligned target segment.

Style Guide

• Enabled: True
• Guide: /home/runner/work/hugging-face-krew.github.io/hugging-face-krew.github.io/workflow/skills/quality/style/hf-blog-ko-translation-guide.md
• Policy: /home/runner/work/hugging-face-krew.github.io/hugging-face-krew.github.io/workflow/skills/quality/configs/style_policy.yml
• Style score: 60.0
• Rule hits: {'alt_text_caption': 1, 'information_addition': 3, 'link_text_translation': 50, 'list_consistency': 1, 'modal_strength': 22, 'overstatement': 1}

Style Guide Findings

Rule	Severity	Segment	Current	Suggested
list_consistency	minor		phrase, phrase, phrase, phrase, sentence, sentence	Use either sentence-style endings or phrase-style endings consistently within one list.
modal_strength	major	p_019	이 프로젝트는 그 초창기의 작업들과 더 가까운 느낌입니다. Surya는 자신의 논문에서 여기까지 오게 된 과정을 설명합니다. 그는 텍스트-이미지 모델에 프롬프트를 입력하는 것에서 시작했습니다. 프롬프트가 조작할 수 있는 유일한 레버이고, 세부 정보를 늘려 제어력을 높이는 데도 한계가 있습니다. 모델 자체를 학습하면 더 나아갈 수 있습니다. 아이디어의 다른 절반은 매체입니다. 모델은 이미지를 그리는 약 150줄의 JavaScript 프로그램을 작성합니다. 모델의 출력은 코드입니다. 이를 읽고, 수정하고, 다시 실행할 수 있으며, 각 붓질 뒤의 결정이 드러납니다. 스타일은 모델이 라이브러리의 메서드 10개만 사용할 수 있도록 제한한 데서 나옵니다. 이에 대해서는 아래에서 더 설명하겠습니다.	Preserve the strength of up to using: 최대.
modal_strength	major	p_022	최근 언어 모델 RL 작업의 대부분은 검증할 수 있는 reward를 사용합니다. 예를 들어 정답이 알려진 수학 문제, 테스트를 통과하는 코드, 또는 실행 비용이 저렴하고 맞거나 틀렸다고 판별할 수 있는 grader가 있습니다. 이 프로젝트는 인간의 선호로부터 보상 모델을 학습하는 과거의 예외적인 방식인 RLHF에 더 가깝습니다.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	p_023	여기서 reward는 미적 선호입니다. 정답은 없습니다. 이 프로젝트의 진짜 질문은 취향에 대해 RL을 수행할 수 있는가입니다.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	p_030	HPSv3은(는) 공개된 7B preference model입니다. 이미지와 텍스트 설명을 입력하면 사람이 해당 이미지를 얼마나 선호할지에 대한 점수를 반환합니다. 이미지 쌍 사이의 인간 선택으로 이루어진 대규모 데이터셋으로 학습되었으므로, 그 점수는 많은 사람의 취향을 평균낸 것입니다. pairwise judge는 Qwen3-VL-30B-A3B-Instruct이며, HF Inference Providers를 통해 호출하는 일반 vision model입니다. pairwise judge는 후보 그림을 pool에서 무작위로 선택한 네 개의 reference와 나란히 보고, 무엇을 중점적으로 평가할지(번짐, 반투명한 워시, 부드러운 가장자리)를 설명한 지침을 따릅니다. 각 비교는 두 가지 제시 순서로 모두 수행하며, 점수는 후보가 이긴 비교의 비율입니다. 유일한 기준은 pool이므로, 그 점수는 해당 평가에 반영된 제 취향입니다.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	p_038	먼저 hps-only부터 시작해 파이프라인이 실제로 학습할 수 있는지 검증했습니다. reward가 상승하고 metric이 정상적이라는 것을 확인한 뒤에는 더 오래 실행할 이유가 없었으므로, 대신 두 개의 장기 run을 시작했습니다. 장기 run이 던지는 질문은 HPSv3의 능력 중 얼마나 많은 부분을 pairwise judge에 넘길 수 있는가입니다. judge의 가중치가 클수록 reward는 모두의 취향이 아니라 제 취향을 더 많이 의미하고, 상승하기도 더 어려워집니다. 더 밀어붙이거나 스타일이 평균에서 너무 멀어지면 모델이 아예 멈출 수도 있습니다.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	p_038	먼저 hps-only부터 시작해 파이프라인이 실제로 학습할 수 있는지 검증했습니다. reward가 상승하고 metric이 정상적이라는 것을 확인한 뒤에는 더 오래 실행할 이유가 없었으므로, 대신 두 개의 장기 run을 시작했습니다. 장기 run이 던지는 질문은 HPSv3의 능력 중 얼마나 많은 부분을 pairwise judge에 넘길 수 있는가입니다. judge의 가중치가 클수록 reward는 모두의 취향이 아니라 제 취향을 더 많이 의미하고, 상승하기도 더 어려워집니다. 더 밀어붙이거나 스타일이 평균에서 너무 멀어지면 모델이 아예 멈출 수도 있습니다.	Preserve the strength of should using: 좋습니다, 해야 합니다.
modal_strength	major	p_047	Part of one rollout's draw() and what it renders to. The comments are the model's own. Reward 0.864, 129 lines, step 22. The full source of every painting is in the rollouts dataset.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	p_051	gate는 마지막 구성 요소입니다. 스케치는 컴파일되어야 하고, 직접 p5 호출 대신 라이브러리를 사용해야 하며, 캔버스에 실제 안료를 칠해야 하고, 예를 들어 캔버스에 텍스트를 쓰는 식으로 scorer를 속이려 해서는 안 됩니다.	Preserve the strength of can using: 수 있습니다.
modal_strength	major	t_058	Qwen3.5-122B-A10B | 22	Preserve the strength of can using: 수 있습니다.

Issues

QL-001 formatting / critical

• Message: link target mismatch.
• Target: https://github.com/acamposuribe/p5.brush
• Suggested fix: Preserve source link target exactly.
• Reason: Hard gate exact-match validator failed: extra=['https://github.com/acamposuribe/p5.brush']

QL-002 technical / major

• Message: Python/API identifier mismatch.
• Source: p5.brush, p5.brush, p5.brush, p5.brush, p5.brush, p5.brush, p5.js, p5.js
• Suggested fix: Preserve source Python/API identifier exactly.
• Reason: Review gate exact-match validator failed: missing=['p5.brush', 'p5.brush', 'p5.brush', 'p5.brush', 'p5.brush', 'p5.brush', 'p5.js', 'p5.js']

QL-003 technical / major

• Message: number/unit token mismatch.
• Source: 1.5
• Target: 0, 0.5, 1, 1, 1, 1, 1, 10
• Suggested fix: Preserve source number/unit token exactly.
• Reason: Review gate exact-match validator failed: missing=['1.5']; extra=['0', '0.5', '1', '1', '1', '1', '1', '10']

QL-004 accuracy / major

• Message: Source segment coverage is low.
• Source: source_segments=150
• Target: target_segments=141
• Suggested fix: Check for omitted paragraphs, headings, list items, or table cells.
• Reason: Segment count validator found fewer target text segments than source text segments.

QL-005 accuracy / major

• Message: QE metric score is low.
• Target: 0.4870
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-006 accuracy / major

• Message: QE metric score is low.
• Target: 0.4367
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-007 accuracy / major

• Message: QE metric score is low.
• Target: 0.3167
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-008 accuracy / major

• Message: QE metric score is low.
• Target: 0.0353
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-009 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0500
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-010 accuracy / major

• Message: QE metric score is low.
• Target: 0.3232
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-011 accuracy / major

• Message: QE metric score is low.
• Target: 0.1894
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-012 accuracy / major

• Message: QE metric score is low.
• Target: 0.4875
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-013 accuracy / major

• Message: QE metric score is low.
• Target: 0.3771
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-014 accuracy / major

• Message: QE metric score is low.
• Target: 0.3018
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-015 accuracy / major

• Message: QE metric score is low.
• Target: 0.3012
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-016 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0342
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-017 accuracy / major

• Message: QE metric score is low.
• Target: 0.2982
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-018 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0323
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-019 accuracy / major

• Message: QE metric score is low.
• Target: 0.3766
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-020 accuracy / major

• Message: QE metric score is low.
• Target: 0.5291
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-021 accuracy / major

• Message: QE metric score is low.
• Target: 0.3923
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-022 accuracy / major

• Message: QE metric score is low.
• Target: 0.4551
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-023 accuracy / major

• Message: QE metric score is low.
• Target: 0.1379
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-024 accuracy / major

• Message: QE metric score is low.
• Target: 0.1109
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-025 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0695
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-026 accuracy / major

• Message: QE metric score is low.
• Target: 0.3771
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-027 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0714
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-028 accuracy / major

• Message: QE metric score is low.
• Target: 0.1696
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-029 accuracy / major

• Message: QE metric score is low.
• Target: 0.3076
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-030 accuracy / major

• Message: QE metric score is low.
• Target: 0.3806
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-031 accuracy / major

• Message: QE metric score is low.
• Target: 0.3930
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-032 accuracy / major

• Message: QE metric score is low.
• Target: 0.3327
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-033 accuracy / major

• Message: QE metric score is low.
• Target: 0.0000
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-034 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0719
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-035 accuracy / major

• Message: QE metric score is low.
• Target: 0.4957
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-036 accuracy / major

• Message: QE metric score is low.
• Target: 0.2901
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-037 accuracy / major

• Message: QE metric score is low.
• Target: 0.3065
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-038 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0314
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-039 accuracy / major

• Message: QE metric score is low.
• Target: 0.3474
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-040 accuracy / major

• Message: QE metric score is low.
• Target: 0.4917
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-041 accuracy / major

• Message: QE metric score is low.
• Target: 0.1864
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-042 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0695
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-043 accuracy / major

• Message: QE metric score is low.
• Target: 0.0000
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-044 accuracy / major

• Message: QE metric score is low.
• Target: 0.0018
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-045 accuracy / major

• Message: QE metric score is low.
• Target: 0.0211
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-046 accuracy / minor

• Message: Embedding similarity is an outlier.
• Target: 0.0386
• Suggested fix: Review whether the segment still corresponds to the source.
• Reason: Embedding similarity is below threshold 0.08.

QL-047 accuracy / major

• Message: QE metric score is low.
• Target: 0.4802
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-048 accuracy / major

• Message: QE metric score is low.
• Target: 0.4798
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-049 accuracy / major

• Message: QE metric score is low.
• Target: 0.0000
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-050 accuracy / major

• Message: QE metric score is low.
• Target: 0.2523
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-051 accuracy / major

• Message: QE metric score is low.
• Target: 0.2101
• Suggested fix: Review this segment for omission, unrelated translation, or over-compression.
• Reason: QE score is below threshold 0.55.

QL-052 accuracy / major

• Message: MQM judge reported accuracy issue.
• Source: Training a coding model to paint watercolours
• Target: Training a coding model to paint watercolours
• Suggested fix: 코딩 모델을 학습해 수채화 그리기
• Reason: 제목이 번역되지 않아 한국어 독자에게 핵심 내용이 전달되지 않습니다.

QL-053 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: Surya Narreddi posted a beautiful video of watercolours painted by a language model.
• Target: Surya Narreddi이(가) 언어 모델이 그린 수채화의 아름다운 영상을 게시했습니다.
• Suggested fix: Surya Narreddi는 언어 모델이 그린 아름다운 수채화 영상을 게시했습니다.
• Reason: 이름 뒤에 기계적으로 붙은 '이(가)'가 한국어 문장을 부자연스럽게 만들며, 수동적인 주어 표기도 블로그 문체에 맞지 않습니다.

QL-054 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: The model writes JavaScript through p5.brush, a library that "adds natural drawing tools to p5.js".
• Target: 이 모델은 p5.brush을(를) 통해 JavaScript를 작성합니다.
• Suggested fix: 이 모델은 "p5.js에 자연스러운 드로잉 도구를 추가하는" 라이브러리인 p5.brush를 사용해 JavaScript를 작성합니다.
• Reason: 제품명 뒤의 기계적인 '을(를)' 표기가 부자연스럽습니다. 또한 뒤 문장에서 라이브러리의 설명을 별도 문장으로 풀어 쓴 구조는 가능하지만, 현재 문장에서는 '통해'보다 자연스러운 연결이 적절합니다.

QL-055 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: The video went viral fast, over 1.5M views at the time of writing.
• Target: 영상은 빠르게 바이럴되었고, 이 글을 쓰는 시점에 조회 수가 150만 회를 넘었습니다.
• Suggested fix: 영상은 빠르게 확산되었으며, 이 글을 쓰는 시점에 조회 수가 150만 회를 넘었습니다.
• Reason: '바이럴되었고'는 이해할 수 있지만 기술 블로그 문맥에서 다소 직역투이며, 원문의 빠르게 확산된 의미를 더 자연스럽게 표현할 수 있습니다.

QL-056 fluency / major

• Message: MQM judge reported fluency issue.
• Source: The video came with a blog post explaining the training behind an earlier and narrower stage of the project, close-up flowers rather than the full compositions in the video, sadly without open artifacts yet.
• Target: 영상에는 프로젝트의 더 이르고 범위가 좁은 단계, 즉 안타깝게도 아직 오픈 artifact는 없는 영상 속 전체 구성 대신 클로즈업한 꽃을 학습한 과정을 설명하는 a blog post도 함께 올라왔습니다.
• Suggested fix: 영상과 함께 프로젝트의 초기 단계이자 범위가 더 좁았던 작업을 설명하는 블로그 글도 공개되었습니다. 영상의 전체 구성이 아니라 클로즈업한 꽃을 다룬 작업이며, 안타깝게도 아직 공개 아티팩트는 없습니다.
• Reason: 영어와 한국어가 혼용되고 수식 관계가 지나치게 얽혀 문장이 부자연스럽고 의미를 파악하기 어렵습니다. 특히 ‘sadly without open artifacts yet’가 프로젝트 단계가 아니라 꽃을 학습한 과정에 잘못 연결될 수 있습니다.

QL-057 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: a blog post
• Target: a blog post
• Suggested fix: 블로그 글
• Reason: 일반 명사구가 번역되지 않은 채 남아 있어 한국어 문장의 완결성과 자연스러움을 해칩니다.

QL-058 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: His site says a full technical report is coming, so ensure you follow him.
• Target: 그의 사이트에 따르면 전체 기술 보고서가 공개될 예정이니, 그를 팔로우해 두세요.
• Suggested fix: 그의 사이트에 따르면 전체 기술 보고서가 곧 공개될 예정이므로, 계속 소식을 확인해 보세요.
• Reason: 의미는 대체로 전달되지만 ‘그를 팔로우해 두세요’는 한국어 기술 블로그 문맥에서 다소 기계적으로 들립니다.

QL-059 fluency / major

• Message: MQM judge reported fluency issue.
• Source: The original idea is his, coming from the art and design side, where his skills are way beyond mine.
• Target: 원래 아이디어는 예술과 디자인 측면에서 출발한 그의 아이디어이며, his skills are way beyond mine.
• Suggested fix: 원래 아이디어는 그의 것이며, 예술과 디자인 분야에서 출발했습니다. 그쪽에서 그의 실력은 저보다 훨씬 뛰어납니다.
• Reason: 문장 후반의 영어가 번역되지 않아 핵심 비교 의미가 누락되었고, ‘그의 아이디어는 ... 그의 아이디어’처럼 중복되어 어색합니다.

QL-060 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: My attempt is on the engineering side, reproducing the recipe in the open with every piece published.
• Target: 제가 시도한 것은 엔지니어링 측면에서의 접근으로, 모든 구성 요소를 공개한 채 레시피를 오픈 방식으로 재현하는 것입니다.
• Suggested fix: 제가 시도한 것은 엔지니어링 측면의 접근으로, 모든 구성 요소를 공개하고 레시피를 오픈 소스로 재현하는 것입니다.
• Reason: ‘오픈 방식으로’는 영어식 표현이 반복되어 다소 어색합니다. 원문의 공개 재현이라는 의미는 유지하면서 자연스럽게 표현할 수 있습니다.

QL-061 accuracy / major

• Message: MQM judge adequacy score is low.
• Target: 0.6800
• Suggested fix: Review this segment against the MQM judge feedback.
• Reason: MQM adequacy score is below threshold 0.75.

QL-062 accuracy / major

• Message: MQM judge reported accuracy issue.
• Source: **Note:** for the context behind the project, told by Surya himself, watch [this
• Target: **참고:** Surya 본인이 직접 설명하는 프로젝트의 배경이 궁금하다면 [this
• Suggested fix: 후속 세그먼트의 링크 텍스트와 URL을 포함해 문장을 완성하세요. 예: '참고: Surya 본인이 직접 설명하는 프로젝트의 배경은 이 영상에서 확인할 수 있습니다.'
• Reason: 원문과 번역문이 모두 링크 문구 중간에서 끝나 있어 안내 문장이 완결되지 않았습니다. 현재 형태로는 무엇을 시청해야 하는지 전달되지 않아 게시용 문장으로 사용할 수 없습니다.

QL-063 fluency / major

• Message: MQM judge fluency score is low.
• Target: 0.7000
• Suggested fix: Review this segment against the MQM judge feedback.
• Reason: MQM fluency score is below threshold 0.75.

QL-064 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: video of his thesis](https://vimeo.com/1190839818).
• Target: video of his thesis](https://vimeo.com/1190839818)을(를) 시청하세요.
• Suggested fix: 학위 논문 영상](https://vimeo.com/1190839818)을 시청하세요.
• Reason: 링크 텍스트인 'video of his thesis'가 영어로 남아 있고, 한국어 조사도 '을(를)' 형태로 기계적으로 표기되어 문장이 자연스럽지 않습니다. URL은 올바르게 유지되었습니다.

QL-065 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: The reference pool dataset, the RL environment, the training scripts and the trained models, all open.
• Target: 참조 pool 데이터셋, RL 환경, 학습 스크립트와 학습된 모델을 모두 공개합니다.
• Suggested fix: 참조 풀 데이터셋, RL 환경, 학습 스크립트와 학습된 모델을 모두 공개합니다.
• Reason: reference pool을 참조 pool로 옮겨 한국어 문장 안에서 부자연스럽고 의미가 불명확합니다. 원문의 공개 대상이 reference pool dataset임을 자연스럽게 전달하려면 용어를 한국어 문맥에 맞게 구성해야 합니다.

QL-066 fluency / minor

• Message: MQM judge reported fluency issue.
• Source: with TRL and OpenEnv
• Target: TRL 및 OpenEnv을(를) 사용해
• Suggested fix: TRL 및 OpenEnv를 사용해
• Reason: 보존된 제품명 뒤에 을(를)을 붙이는 표기는 기계적인 번역투이며, 한국어 문장에서 부자연스럽습니다. 조사 생략 또는 자연스러운 조사를 사용해야 합니다.

QL-067 fluency / major

• Message: MQM judge reported fluency issue.
• Source: the RL environment and the scorer model as Spaces
• Target: RL 환경과 scorer 모델은 Spaces(으)로
• Suggested fix: RL 환경과 scorer 모델을 Spaces로
• Reason: 조사 선택 placeholder인 “(으)로”가 남아 있고, 문장 성분의 관계가 불분명해 자연스러운 한국어로 읽히지 않습니다. 원문의 두 항목을 Spaces로 제공한다는 의미가 명확하게 드러나야 합니다.

QL-068 accuracy / major

• Message: MQM judge adequacy score is low.
• Target: 0.7200
• Suggested fix: Review this segment against the MQM judge feedback.
• Reason: MQM adequacy score is below threshold 0.75.

QL-069 fluency / minor

• Message: MQM judge reported fluency issue.
• S

SEO report — ✅ Pass

SEO Eval Report

Gate: ✅ PASS — deterministic AND rubric

• File: ../target/_posts/2026-09-03-train-to-paint-with-code.md
• Source: —
• Primary keyword: (none — D5 skipped)
• Mode: file

Gate

• Status: PASS
• Blockers: ✅ pass
• Deterministic REQUIRED (D1–D7): ✅ pass
• Rubric (R1–R6): ✅ pass (mean None, min None)

Blockers

✅ body_not_empty: Body is not empty
✅ robots_indexable: Robots is indexable
✅ internal_links_resolve: All internal links resolve
✅ local_images_resolve: All local images resolve

Required checks (gated)

✅ heading_hierarchy: Heading hierarchy: Valid
✅ alt_text_coverage: Alt text coverage: 11/11 images
✅ descriptive_alt_text: Descriptive alt text: 11/11 (≥80% recommend)
✅ image_files_exist: All 0 local image file(s) exist

OpenAI rubric checks

✅ semantic_metadata: PASS (required) — Title and H1 match in meaning; opening confirms translation source; headings align with the topic.
✅ alt_semantics: PASS (review) — https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-to-paint-with-code/thumbnail.png: 목적과 산출물(수채화)을 명확하게 설명; https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-to-paint-with-code/reward-diagram.png: 파이프라인의 구성 요소를 단계별로 설명; https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-to-paint-with-code/sketch-and-render.png: 생성된 스케치와 해당 페인팅의 관계를 함께 설명

Advisory checks (not gated)

🟠 opening_summary: Opening 3 paragraphs: 31 words (recommend ≥50 for GEO)
✅ h1_count: Markdown H1 count: 1 (review against rendered layout)
✅ citations: Citations/statistics: 66 (recommend ≥1 for GEO)
✅ question_headings: Scannable H2/H3 (question or keyword): 1 (1 question, 0 keyword)
⚠️ internal_links: Internal links: 1 (recommend 2-3)
✅ word_count: Word count: 3960 (recommend ≥300)
ℹ️ primary_keyword: No primary_keyword in manifest — keyword check skipped
⚠️ webp_format: WebP format: 0/11 images (≥50% recommend)
⚠️ lazy_loading: Lazy loading: 0 images (optional)

Signals (evidence — not directly gated)

• Frontmatter: title 33 chars, description 0 chars, author present True
• Title text: TRL과 OpenEnv로 수채화를 그리는 코딩 모델 학습하기
• Description text: —
• Opening text: * TOC
  {:toc}

이 글은 Hugging Face 블로그의 Training a coding model to paint watercolours with TRL and OpenEnv를 한국어로 번역한 글입니다.

• Opening: first paragraph 187 chars, first 3 paragraphs 365 chars
• Headings: markdown H1 1, rendered effective H1 2, layout title H1 True
• Links: total 64, external 63, internal 0, citation signals 66
• Images: total 11, empty alt 0, filename-like alt 0, missing local files 0

Semantic review packet

• Title: TRL과 OpenEnv로 수채화를 그리는 코딩 모델 학습하기
• Description: —
• Rendered H1 candidates: TRL과 OpenEnv로 수채화를 그리는 코딩 모델 학습하기, TRL과 OpenEnv로 수채화를 그리는 코딩 모델 학습하기
• Opening: * TOC
  {:toc}

이 글은 Hugging Face 블로그의 Training a coding model to paint watercolours with TRL and OpenEnv를 한국어로 번역한 글입니다.

• Canonical/permalink: —
• Instruction: Compare title, description, rendered H1, and opening text for meaning consistency. This packet is evidence only; it does not decide pass/fail.

Frontmatter (advisory — written by metadata step, not gated)

✅ title: Title: 33 chars (recommend ≤60)
❌ description: Description is missing
✅ image: OG image: assets/images/blog/posts/2026-09-03-train-to-paint-with-code/thumbnail.png
✅ categories: Categories: 2 (recommend 2-3)
✅ author: Author: dailybot

SEO metadata suggestion — PARTIAL

This is a suggestion. SEO is applied only when the post frontmatter is updated.
To apply safe fields from a partial suggestion, leave a trusted PR comment: metadata apply.

• Auto apply: False
• Requires human: True
• Mode: frontmatter_only
• Reason: metadata candidate needs policy decisions or missing title/description

Candidate

• title: TRL과 OpenEnv로 수채화를 그리는 코딩 모델 학습하기
• categories: ['Translation', 'HuggingFace']
• image: assets/images/blog/posts/2026-09-03-train-to-paint-with-code/thumbnail.png

Needs policy decision

• target_url
• source_url
• canonical_policy
• translation_indexing
• target_locale
• source_locale

Warnings

• frontmatter.description is empty
• content.source_url is missing