• Home
  • Technology
  • Gaming
  • Entertainment
  • World & Business
  • Science
  • Sports
  • AI
HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • Home
    • Technology
    • Gaming
    • Entertainment
    • World & Business
    • Science
    • Sports
    • AI
    AI
    Report

    ‘Curriculum Learning as Transport’ brings a geometric view of curriculum learning to COLM 2026

    A researcher said former MSR intern Changho Shin was presenting the poster on October 8, examining pacing, exposure and abruptness.

    David Alvarez MelisDA
    2 Sources, 2h ago, first seen 2h ago

    TLDR

    On October 8, a researcher said former MSR intern Changho Shin was presenting ‘Curriculum Learning as Transport’ at a COLM 2026 poster session. The researcher described the work as a geometric reframing of curriculum learning as training along distributional paths, intended to clarify concepts including pacing, exposure and abruptness.

    Combined views

    290

    2 Sources, first seen 2h ago

    Combined views

    290

    2 Sources, first seen 2h ago

    10 likes
    10 likes
    2 comments
    1 saves
    2 comments
    1 saves

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Today's Rank

    —

    Not ranked yet

    Today's Rank

    —

    Not ranked yet

    2 Sources

    David Alvarez Melis@elmelis@kimiahmdh is presenting "Domain-Aware Scaling Laws Uncover Data Synergy": extends Chinchilla by adding pairwise domain interactions to the scaling law. Fitted observationally across 52 open-weight LLMs, it recovers known (+ unexpected!) domain synergies https://arxiv.org/abs/2607.110522h

    2 Sources

    David Alvarez Melis@elmelis@kimiahmdh is presenting "Domain-Aware Scaling Laws Uncover Data Synergy": extends Chinchilla by adding pairwise domain interactions to the scaling law. Fitted observationally across 52 open-weight LLMs, it recovers known (+ unexpected!) domain synergies https://arxiv.org/abs/2607.110522h