#imitationlearning
Live, measured metrics for the hashtag #imitationlearning from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #imitationlearning
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 2026-11-01 17:34 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 2026-11-01 17:34 UTCNo related tags with measured usage found for #imitationlearning.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-11-01 17:34 UTCEverything below is measured over the latest 14 public posts (spanning ~54507 hours).
Posting hours (UTC) — busiest: 16:00
Languages: English (13) · Polish (1)
Avg boosts / post: 0.1
Top of the latest posts
fly51fly (@fly51fly) UC Berkeley의 S. Park과 S. Levine이 행동 복제(Behavioral Cloning)를 다룬 신규 연구 「Behavioral Cloning Mystery」를 공개했습니다. 에이전트·로보틱스 정책 학습에서 데모 기반 모방학습의 성능 및 동작 원인을 분석하는 연구로 보입니다. https://x.com/fly51fly/status/2107946652627792015 #beha
fly51fly (@fly51fly) Stanford·CMU 연구진이 컴퓨터 사용(computer-use) 실행 트레이스에서 특정 작업용 모델을 유도하는 방법을 제안한 논문입니다. 에이전트가 수행한 UI 조작·작업 기록을 활용해 새로운 태스크 모델을 만드는 접근으로, 컴퓨터 사용 에이전트의 데이터 효율 및 작업 특화 학습과 관련됩니다. https://x.com/fly51fly/status/2090919332067078630 #
fly51fly (@fly51fly) EPFL 연구진이 가치 기반 모방학습(value-based imitation learning)에서 온폴리시 상호작용이 언제 도움이 되는지와 표현 학습의 트레이드오프를 다룹니다. 오프라인 데모 데이터만 쓰는 학습과 환경 상호작용을 추가하는 학습의 차이를 분석하는 강화학습 연구입니다. https://x.com/fly51fly/status/2084396305431085453 #reinforcem
#imitationlearning across platforms
every network with a public tag surfaceFollow #imitationlearning 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/imitationlearning