#rlhf
Live, measured metrics for the hashtag #rlhf from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #rlhf
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-10-14 21:42 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-10-14 21:42 UTCLive pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-10-14 21:42 UTCEverything below is measured over the latest 40 public posts (spanning ~4891 hours).
Posting hours (UTC) — busiest: 07:00
Languages: English (23) · Russian (12) · Czech (1) · German (1) · Chinese (Taiwan) (1)
Avg boosts / post: 0.2
Top of the latest posts
Mojofull (@furoku) AI 세션 평가가 매일 자동으로 이뤄지는 환경이라면, 기업은 ZentouAI를 직원에게 쓰게 하는 것만으로도 1년 뒤 대부분의 업무를 AI가 대체할 수 있다고 말한다. 매일 최적의 업무 프로세스와 산출물이 선택되며, RLHF 수준을 넘어서는 변화라는 주장이다. https://x.com/furoku/status/2040235067197501917 #ai #automation #rlhf #ente
fly51fly (@fly51fly) Stanford·토론토 연구진이 인간 선호를 검증 가능한(verifiable), 언어로 명시 가능한(articulable), 암묵적(tacit) 요소로 구분해 다루는 프레임워크를 제안했다. LLM 정렬·RLHF에서 어떤 선호를 명세·평가·학습할 수 있는지 분석하는 연구로 보인다. https://x.com/fly51fly/status/2107230553103896615 #aialignment
Archer Chang | AI for Learning (@archerchang_ai) 장기적으로 볼 만한 AI 학습·실무 자료를 만드는 인물들을 추천한다. Andrej Karpathy의 모델 원리·from-scratch 구현, Simon Willison의 LLM 실험과 도구 사용 경험, Lilian Weng의 Transformer·RLHF·에이전트 해설, Sebastian Raschka의 논문/PyTorch 구현, swyx의
What “rlhf” means
WikipediaIn machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning.
“Reinforcement learning from human feedback” on Wikipedia (CC BY-SA) →#rlhf across platforms
every network with a public tag surfaceFollow #rlhf straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/rlhf