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Agents-A1 achieves 1T-model performance through long-task training, not bigger parameters

Shanghai AI Laboratory's Agents-A1, a 35B-parameter model, matches trillion-scale rivals through extended trajectory training and multi-teacher

Raccontata dacryptobriefing.comdev.to

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AI · summaries
cryptobriefing.comStai leggendo1 g fa

Agents-A1 achieves 1T-model performance through long-task training, not bigger parameters

Shanghai AI Laboratory's Agents-A1, a 35B-parameter model, matches trillion-scale rivals through extended trajectory training and multi-teacher

originale
dev.to1 g fa

A 35-billion-parameter agent that punches like a trillion-parameter model

Shanghai AI Lab argues you can reach giant-model performance on long tasks not by adding parameters, but by training on much longer chains of real work.

Leggi questa versione → originale

Timeline cronologica

  1. mercoledì 1 luglio 2026·cryptobriefing.com

    Agents-A1 achieves 1T-model performance through long-task training, not bigger parameters

    Shanghai AI Laboratory's Agents-A1, a 35B-parameter model, matches trillion-scale rivals through extended trajectory training and multi-teacher

  2. mercoledì 1 luglio 2026·dev.to

    A 35-billion-parameter agent that punches like a trillion-parameter model

    Shanghai AI Lab argues you can reach giant-model performance on long tasks not by adding parameters, but by training on much longer chains of real work.