#CurriculumLearning
Live, measured metrics for the hashtag #CurriculumLearning from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #curriculumlearning
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 2026-10-02 00:00 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 2026-10-02 00:00 UTCNo related tags with measured usage found for #curriculumlearning.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-10-02 00:00 UTCEverything below is measured over the latest 3 public posts (spanning ~5468 hours).
Posting hours (UTC)
Languages: English (3)
Avg boosts / post: 0
Top of the latest posts
HackerNewsTop5 (@hackernewstop5) LLM이 초등학교 5학년 수준을 넘는 자료를 전혀 학습하지 않았을 때 어떤 언어·추론·지식 능력을 보이는지 탐구하는 프로젝트다. 학습 데이터의 난이도와 범위가 모델 역량 형성에 미치는 영향을 관찰할 수 있어, 데이터 큐레이션·커리큘럼 학습·소형 모델 연구 관점에서 참고할 만하다. https://x.com/hackernewstop5/status/20889016397345
Prowl: Learning Through Discovery PROWL은 강화학습(RL) 기반의 적대적 프레임워크로, RL 에이전트가 게임 환경을 탐색하며 월드 모델의 실패 사례를 발견하고 이를 학습 커리큘럼으로 활용해 모델 성능을 개선한다. Minecraft 환경에서 평가된 PROWL은 행동 추종 정확도, 시각적 품질, 환경 동역학 등에서 기존 모델 대비 우수한 성능을 보였으며, 점진적 난이도 조절을 통해 안정적인 적대적 학
Scaffolding to Superhuman: How Curriculum Learning Solved 2048 and Tetris https://kywch.github.io/blog/2025/12/curriculum-learning-2048-tetris/ #HackerNews #Scaffolding #Superhuman #CurriculumLearning #2048 #Tetris
#curriculumlearning across platforms
every network with a public tag surfaceFollow #curriculumlearning 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/curriculumlearning