'rl from ai feedback' 검색 결과

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Extracts full item detail from any open eBay item URL, returning JSON with url, itemNumber, title, subTitle, categories, price, priceWithCurrency, currency, wasPrice, available, availableText, sold, image, images, seller, sellerUrl, sellerFeedbackCount, sellerPositiveRating, itemLocation, brand, type, mpn, upc, ean, condition, shippingCost, shipping, whyToBuy, and itemSpecifics (full label/value map). Works across all eBay regional TLDs. Use when user mentions eBay item page, eBay listing detail, eBay product page, eBay itm, ebay.com/itm, eBay item scraper, extract eBay item details, eBay item specifics, scrape eBay item, eBay sold count, eBay availability, eBay brand MPN UPC EAN, eBay item location, eBay seller feedback, eBay wasPrice, eBay hi-res images, eBay itemNumber, eBay per-item enrichment. Also applies to enriching item URLs from a listing, monitoring price and stock, competitive product research, brand catalog audits, and pulling full field set from an individual eBay listing.

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Método da Anthropic para treinar IA inofensiva através de auto-aprimoramento. Abordagem em duas fases - aprendizado supervisionado com auto-crítica/revisão, depois RLAIF (RL from AI Feedback). Use para alinhamento de segurança, redução de outputs prejudiciais sem rótulos humanos. Alimenta o sistema de segurança do Claude.

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Choose a model training method and configuration from paired response preferences or individual positive and negative ratings. Covers DPO, ORPO, KTO, and SimPO, including pair construction, repeated training rounds, and troubleshooting learning settings.

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• Choose a training method for paired response preferences
• Select a method for individual positive and negative ratings
• Review learning settings when DPO training loses general capability

Manage job searches through modes for evaluating vacancies, tailoring CVs, tracking applications, drafting outreach, and preparing for interviews. Start from a job description or URL, or choose a specific task to produce relevant documents and status summaries.

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• Compare vacancies and prioritize applications
• Create a tailored CV and application email draft
• Track application status and overdue follow-ups

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

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공개 사이트와 API에서 채용 공고, 가격, 뉴스 등을 정기 수집하는 시스템을 만듭니다. AI 요약·분류·평가, 저장소 연동, 사용자 선택을 다음 평가에 반영하는 설계를 다룹니다.

• 채용 공고 수집 및 조건 일치도 평가
• 상품 가격 정기 추적
• 뉴스 수집 및 주제별 분류

Check earlier Paperclip tasks for verified progress and actual deliverables, then report findings with links. When requested, add feedback as the connected person, with safeguards against posting the same comment twice.

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• Check progress and deliverables from earlier tasks
• Add feedback to a task as the connected person
• Monitor task updates in an environment that supports Events

Open Plannotator’s browser-based code review interface from Kiro and address the returned feedback. Review the current directory, another working directory, or a pull request URL, and optionally choose a comparison base to focus the changes shown.

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• Review code changes from Kiro
• Review a separate working directory
• Open a review for a pull request URL

Review code changes through a structured check of requirements, design, correctness, security, performance, and tests. Produce specific, constructive feedback that separates required fixes from optional suggestions, and build shared review guidelines for a development team.

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• Review a pull request systematically
• Create a shared code review checklist
• Give actionable feedback to junior developers

Review a Figma design, screenshot, or written description for usability, visual hierarchy, consistency, and accessibility. Receive specific findings, explanations, and prioritized improvements, with feedback tailored to the design stage and intended users.

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• Reviewing an early screen design
• Checking navigation and onboarding usability
• Finding inconsistent colors, spacing, and typography

Review a Figma design, screenshot, or written description for usability, visual hierarchy, consistency, and accessibility. Receive specific findings, explanations, and prioritized improvements, with feedback tailored to the design stage and intended users.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an early screen design
• Checking navigation and onboarding usability
• Finding inconsistent colors, spacing, and typography

Helps an agent take over an existing role after a planned handover. It guides checks of inherited records, identity, authority, and recurring duties, while explaining how to recover the predecessor’s reasoning and preserve lessons for the next agent.

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• Getting oriented after a planned agent handover
• Checking inherited records and recurring duties
• Recovering the reasoning behind earlier decisions

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Split a large pull request into smaller, reviewable changes. Plan branch dependencies, extract related code, verify each part, and track review changes while preserving the intent of the original work.

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• Split a large pull request into reviewable parts
• Separate refactoring from behavior changes
• Plan dependent pull requests

Use Memori through MCP, a protocol connecting agents to tools, to retrieve past decisions and preferences, summarize ongoing work, and resume after conversation compression. It also guides selective long-term memory storage and usage limit checks.

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• Retrieve decisions and preferences from earlier sessions
• Review current work when starting a session
• Resume work after conversation context is compressed

Help law clinic students prepare motions, letters, petitions, and declarations from case notes and templates. Depending on the supervisor’s teaching mode, provide structure, drafting feedback, or a first draft with missing facts and verification points clearly marked.

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• Preparing an eviction answer from case notes
• Organizing an asylum declaration
• Drafting a demand letter for supervisor review

Create Three.js game effects that clearly communicate attacks, hits, damage, and status changes. Define each effect’s purpose and lifetime, limit resource use, and test overlapping effects, repeated triggers, low-quality settings, and reduced-motion alternatives.

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• Distinguish attack warnings from hit effects
• Create damage and status feedback
• Limit particle counts during repeated attacks

This skill helps users extract structured video list data and comment data from YouTube using the BrowserAct API. The Agent should proactively apply this skill when users request searching for YouTube videos and their comments, analyzing viewer sentiment for a specific video topic, gathering audience feedback on AI or automation, extracting a list of top videos and their viewer reactions, compiling YouTube video data along with user opinions, retrieving competitor video titles and related audience discussions, monitoring public response to specific YouTube search keywords, summarizing comments from search results for market research, tracking viewer engagement metrics and replies for trending topics, collecting YouTube video URLs and author details alongside community discussions, or automating the extraction of YouTube comments without manual scraping.

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Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.

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논문과 연구 계획을 공통 기준으로 검토하고 연구 방법, 근거, 인용, 글의 구성을 점검합니다. 평가 이유와 수정 우선순위, 다음 수정 사항을 정리합니다.

• 투고 전 논문 검토
• 인용 문헌의 주장 뒷받침 여부 확인
• 연구 방법과 한계 평가