ActiveSaddler: automated curriculum learning for agent harness optimization
Microsoft + KAIST + POSTECH paper treating the training curriculum itself as a non-stationary bandit that co-evolves with the agent harness; +4.4pp on GAIA2, +7.5pp on Terminal-Bench 2.0.
Why it matters
Identifies a missing dimension in the red-hot harness-optimization space: existing methods fix the training scenarios while only optimizing how the harness updates. The curriculum itself adapts to the harness's evolving weaknesses.
What you could build with it
A platform team running fleets of coding agents could adopt the failure-pattern-bandit idea to auto-prioritize their agent evaluation suites: scenarios where the harness most recently failed get scheduled first. The result: faster harness iteration cycles without bigger eval budgets.
Does it hold up?
Too early to judge: code and project site are not released yet (promised at aka.ms/ActiveSaddler-website); evidence is limited to the paper's own GAIA2 and Terminal-Bench 2.0 experiments.
Built with ActiveSaddler
- ActiveSaddler Adds 7.5pp on Terminal-Bench via Bandit Curriculumaiweekly · AI Weekly summary of the arXiv paper: +4.4pp Pass@1 on GAIA2 and +7.5pp on Terminal-Bench 2.0 from a non-stationary bandit curriculum over failure-pattern arms, with an honest note on missing ablations.
Learn more
First spotted on article: source.
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