Scaling Synthetic Task Generation for Agents via Exploration
Apple Machine Learning Journal / 3/24/2026
💬 OpinionIdeas & Deep AnalysisModels & Research
Key Points
- The paper proposes a method to scale synthetic task generation for agent training by using exploration to discover useful task variations.
- It focuses on improving how agents obtain diverse learning signals without relying solely on manually curated benchmarks or costly human data.
- The approach is positioned as enabling more efficient coverage of task space, which can strengthen agent generalization.
- The work is shared as a research contribution published March 2026 and linked to ICLR.
Post-Training Multimodal Large Language Models (MLLMs) to build interactive agents holds promise across domains such as computer-use, web navigation, and robotics. A key challenge in scaling such post-training is lack of high-quality downstream agentic task datasets with tasks that are diverse, feasible, and verifiable. Existing approaches for task generation rely heavily on human annotation or prompting MLLM with limited downstream environment information, which is either costly or poorly scalable as it yield tasks with limited coverage. To remedy this, we present AutoPlay, a scalable…
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